Cancer Categorization Using Genetic Algorithm to Identify Biomarker Genes.

In the microarray gene expression data, there are a large number of genes that are expressed at varying levels of expression. Given that there are only a few critically significant genes, it is challenging to analyze and categorize datasets that span the whole gene space. In order to aid in the diagnosis of cancer disease and, as a consequence, the suggestion of individualized treatment, the discovery of biomarker genes is essential. Starting with a large pool of candidates, the parallelized minimal redundancy and maximum relevance ensemble (mRMRe) is used to choose the top m informative genes from a huge pool of candidates. A Genetic Algorithm (GA) is used to heuristically compute the ideal set of genes by applying the Mahalanobis Distance (MD) as a distance metric. Once the genes have been identified, they are input into the GA. It is used as a classifier to four microarray datasets using the approved approach (mRMRe-GA), with the Support Vector Machine (SVM) serving as the classification basis. Leave-One-Out-Cross-Validation (LOOCV) is a cross-validation technique for assessing the performance of a classifier. It is now being investigated if the proposed mRMRe-GA strategy can be compared to other approaches. It has been shown that the proposed mRMRe-GA approach enhances classification accuracy while employing less genetic material than previous methods. Microarray, Gene Expression Data, GA, Feature Selection, SVM, and Cancer Classification are some of the terms used in this paper.

1. Introduction

Finding and selecting relevant genes from large amounts of high-dimensional microarray data is the most challenging task to address when analyzing these kinds of data. Because of the ability to detect gene expression levels in DNA microarrays, researchers may get a better understanding of the challenges associated with cancer classification and pave the way for personalized cancer therapy to become a reality. Cancer datasets are often vast in size, and the number of features in a dataset has a substantial influence on the analytical correctness of the data analysis. The absence of a robust approach for analyzing data for all genes at the same time is the most difficult problem to solve. As a consequence, the whole dataset may be condensed down to a small number of differentially expressed genes that may be utilized to discriminate between malignant and noncancerous occurrences of the disease. The most significant task in microarray analysis is the identification of genes that are differentially expressed [1].

Generally speaking, gene selection strategies may be split into three categories: I filter methods, II wrapper methods, and III hybrid methods [2] [all of which are discussed in this section]. Filter approaches allow you to choose genes by searching and ranking them individually, or by selecting a subset of all of the genes in the database using filter techniques. Various metrics are being created for filtering qualities such as information, distance, similarity, consistency, and statistical measures, among other factors, and are being tested in the field. Feature extraction technique consists of single feature which is defined as a univariant and is given as a input to the classifier; these strategies scan through the whole feature space and evaluate all of the possible feature subsets that may be identified. Subsets are evaluated based on the classification performance of the classifier and the clustering performance of the clustering technique (for example, K-means) used for clustering. Even if the performance of some models is outstanding, the computational complexity is increased as a consequence of this.

Hybrid approaches use a variety of unique strategies in order to choose the most suitable subset of the population. Using filter approaches, the feature space is reduced in size, and a wrapping method is then utilized to choose the best candidate subset, resulting in high accuracy and efficiency for the selection process. In the literature, many unique mixed approaches have been proposed, including random forest-based feature selection [3], dynamic genetic algorithm [4], adaptive ant colony optimization [5], and cuckoo search algorithm [4].

According to the planned research, biomarker genes will be discovered and an effective classification model will be developed, which will be capable of identifying the sickness with high accuracy while only needing the identification of a small number of genes. The mRMRe-GA approach that has been proposed consists of two steps for gene selection: the first stage and the second stage, respectively. The parallelized minimum Redundancy and Maximum Relevance ensemble (mRMRe) approach is used in the first stage to choose the optimal subset of genes, which is then applied in the second stage. In the next phase of gene selection, the top m genes from this group are selected using the Genetic Algorithm (GA) using the Mahalanobis Distance as the distance measure, as described before. Last but not least, the SVM classifier is used to generate the classification model since it has lower processing costs and greater classification accuracy when compared to any other nonlinear classifier [5]. Figure 1 depicts a schematic representation of the mRMRe-GA approach reported in this study.

Figure 1

The schematic of the mRMRe-GA method.

A total of four microarray benchmark datasets are evaluated using the mRMRe-GA approach, and the statistical relevance of the proposed method is shown for each of the datasets examined. In the remaining section of the paper, there are six sections, which are as follows: As previously stated, Section 2 outlines the works that are relevant to the suggested method, and the notions of mRMRe and GA are explained in Sections 3 and 4, respectively. Section 2 discusses the works that are pertinent to the proposed technique and how they were completed. Section 5 provides a thorough explanation of the mRMRe-GA approach. A performance evaluation of the recommended approach is shown in Sections 6 and 7, and the project's conclusion is presented in Section 7.

2. Related Works

MI-based ranking criteria are widely used to study the relationships between genes in order to discover feature candidates, and they are becoming more popular. This joint measure represents the relationship between two multidimensional variables. It may be used to partition large datasets into groups and to construct a classification model for classification purposes. In addition, information-theoretic ranking criteria [6,7] take advantage of the relationships that exist between variables and serve as the basic theoretic foundation for a huge number of research publications that use filter approaches. MI is significant in feature selection and subset selection because it has a consistent theoretical foundation when compared to other heuristic approaches. MI is also useful in classification and clustering. When used in combination with class identification, MI is calculated, and relevant traits are emphasized [8,9]. An MI-based group-oriented feature selection strategy has been proposed [9] for selecting features for microarray datasets. Correlation values are obtained from the feature extraction technique, and the classification is carried out using feature extracted values. A SVM-based classification model is proposed in this study [10], which makes use of the LOOCV approach. Genes are prioritized and selected for future investigation based on their MI scores.

The traditional empirical MI-based gene selection approaches suffer from data sparseness owing to the multidimensional nature of microarray data and the multidimensional structure of microarray data, which is a problem for many years. As a result, it was proposed to tackle the issue by using a multivariate Gaussian generative model for predicting the average information content of class variables for feature selection. In the case of this approach [11], the entropy was calculated for the class variables rather than the data. Several feature selection approaches and classification models were combined in Wang et al. [12], and the authors studied the results to see what would happen. The Random Forest approach, which makes use of an ensemble of classification trees to solve gene selection problems, has been proposed for application in gene selection problems [3]. When it comes to evaluating microarray data, researchers have proposed the Genetic Bee Colony approach [13], which combines the Genetic and Artificial Bee Colony algorithms [13]. The mRMR approach was used to reduce the exploration space first, and then the Artificial Bee Colony algorithm was utilized to enhance the gene exploration process by increasing the number of candidates. Several artificial bee colony methods employing SVM classifiers, including correlation and mRMR-based algorithms, have been proposed [14,15] for use in the gene selection process. Peng et al. [16] proposed a GA-based model with an SVM classifier for removing redundant noninformative genes, which was applied in the final version. The results were fine-tuned using the recursive feature elimination (RFE) approach, which was developed by the researchers [17]. Several studies have been conducted to increase the effectiveness of gene prediction using a modified Particle Swarm Optimization (PSO)-based SVM classifier model [18,19]. To make the basic PSO more realistic, it was decided to tweak it in such a manner that only a limited number of particles were randomly selected and the performance of each particle was assessed using a specified fitness value. They proposed an mRMR-based gene selection model, which they said was implemented using a weighted PSO-SVM technique, in their paper [18]. Genes were given different weights, and the PSO improved its parameters based on the weights that were provided to them in order to optimize the selection process for each gene. The SVM classifier5 was tuned using the Adaptive Ant Colony (AACO) optimization approach, which was developed by the University of Pennsylvania. When the classification findings were analyzed, they were used to provide input to the feature convergence optimization process, which in turn was used to optimize the classification results. In Akadi et al. [19], the authors used an mRMR filter approach to increase the overall performance of the GA by boosting the gene selection process of the GA using an SVM classifier to boost the gene selection process of the GA. Several authors, including Gunavathi and Hemalatha [20], have proposed a statistical strategy for gene selection, which is detailed below. These approaches, when combined with GA-SVM/kNN, were utilized to find biomarker genes. In addition to statistical approaches, the cuckoo search optimization algorithm, which was previously reported, was used to increase the efficiency of the gene selection process to improve its effectiveness. For the purpose of ranking the most important qualities, a dynamic GA with an SVM classifier was constructed. 4 Dynamically changing parameters such as chromosome size, recombination operator, probability value, and selection method were all used in the simulation because these factors increased the likelihood of GA reaching the global optimum in a time-efficient manner, and thus these parameters were used in the simulation. mRMR was used in conjunction with two optimization algorithms, Cuckoo and Harmony Search (HS), to increase the efficiency of the gene selection process [21]. Cuckoo and Harmony Search (HS) were used to enhance the efficiency of the gene selection process. The COA-HS classifier was used to categorize the data, with the output of the mRMR classifier serving as an input, and the SVM classifier being used to classify the data. Additional cost figures were estimated and compared to those of other methodologies, and the results were published. In order to find cancer biomarkers, it has been proposed to use a classification method based on a fuzzy rough set [22]. Prediction accuracy was investigated using semi-supervised learning approaches, which are described in detail below.

3. Minimum Redundancy and Maximum Relevance Ensemble (mRMRe) Method

Prediction models in biology are developed by analyzing and comprehending enormous amounts of genetic data. The capacity to do so is particularly important in the creation of prediction models. The inter-correlational interactions between data points play a significant role in determining the effectiveness of prediction models. When dealing with large-scale datasets, it is vital to identify and name the genes that are relevant to the investigation. This is especially true when working with genetic data. Because of its low processing cost [23], the mRMR is a particularly interesting feature selection approach to study in depth. In order to choose relevant qualities that are least redundant while still meeting the highest number of relevance requirements, the mRMR uses the MI value. While mRMR's performance is generally dependable, it does so at the expense of reliability since it picks a whole new feature set when the sample size is modified by a little amount.

mRMRe has minimum Redundancy and Maximum Relevance ensemble (mRMRe), which takes advantage of parallel computing to create numerous feature sets rather than a single feature set, in order to overcome this problem [24].

As part of the basic mRMR, an ensemble learning approach was used to more effectively search for the feature space while making use of parallel computing, as well as to develop robust predictors, which resulted in enhanced overall performance. The use of the mRMRe may be beneficial in applications such as high-throughput genomic data processing, which needs more complete feature space exploration with less bias and variance. When looking for nonredundant, relevant, and informative genes, the mRMR provides functions that search throughout the whole sample space and choose them for further investigation. MI may be used to identify the relevance and redundancy of genes in a population by analyzing their expression patterns.where p and q are the two random variables, and ρ represents the correlation coefficient.

Let q be the input variable and p = {p1,…, p} be a set of input features. The feature set F is framed based on the calculated MI value between features and output variable.

Initially, the feature pi with maximum relevance and minimum redundancy with the class label was added to F. The maximization criterion is as follows:

The above step was repeated until the desired feature set had been achieved.

From equation (2), it is represented that the F Basal, or general, transcription factors are necessary for RNA polymerase to function at a site of transcription in eukaryotes. The maximum repeat range is from 0 to 1.

4. Genetic Algorithm (GA)

It is possible to uncover the most optimum solutions in a broad search area by using biological evolution models, such as GA. First, the algorithm is introduced via the use of a population of randomly generated solutions that represent chromosomes, which acts as the program's initial starting point. In most populations, the size of the population is governed by the number of chromosomes that is handed down from one generation to another. Illustration of the binary alphabet-coded representation of each chromosome is seen in Figure 2. Each chromosome is represented by a vector of variables with a limited number of characters in the binary alphabet to represent it. To fill the population, iteratively shifting chromosomes from another population, referred to as generations, was employed [25]. Genetic operators are used by GA to ensure that genetic variation is maintained over the course of the organization's growth. The progress of evolution is dependent on the existence of genetic variation. [26] In terms of form and function, genetic operators are akin to the processes that occur in real-world biological evolution in terms of their occurrence. The following are the operators in use:

Figure 2

Chromosome representation.

(I) Chromosome selection: Depending on the quality of each chromosome, the fitness value of each chromosome was estimated, and the chromosomes with the greatest fitness values were passed to the next generations.

(II) Chromosome selection: In the case of crossover/recombination, the chromosomes from the chosen set were joined to form a new set of chromosomes, as shown in Figure 3 (crossover/recombination).

Figure 3

Crossover representation.

To get the final outcome, as shown in Figure 4, random alterations were introduced to the binary encoding of chromosomes. This contributes to the preservation of variability among the population while also avoiding the issue of solutions being imposed prematurely (Algorithm 1).

Figure 4

Mutation representation. (a) Before Mutation. (b) After Mutation.

Algorithm 1

The pseudo-code for the algorithm is given below.

5. Proposed mRMRe-GA Method

This section describes the methodology for identifying and selecting biomarker genes using the proposed mRMRe–GA method. A flowchart of the mRMRe–GA method is shown in Figure 5. The mRMRe was used to identify top m informative minimum redundant maximum relevance (mRMR) genes. This method works in parallel so the computational complexity is reduced. It uses mutual information as the statistical measure to identify mRMR.

Figure 5

Flowchart of the proposed mRMRe–GA method.

Termination criteria include:

Whenever the population has not improved after X iterations, the condition is said to be met.

When we achieve a certain number of generations in absolute terms.

Whenever the value of the goal function reaches a specific predetermined threshold.

Genes that were significantly related with the categorization label were chosen using the maximum relevance technique, which was determined as stated in equation (2). It is possible that the highly connected genes are likewise highly reliant on other genes. To accurately identify the informative genes [23], it is thus required to reduce redundancy from the dataset. In order to identify the most informative genes, it was necessary to eliminate redundancy among them. The top m informative genes were then used as input to the GA algorithm. This population was formed from the top m informative genes, which were then utilized to produce the GA [27], which was the GA's initial population. It was determined that the Mahalanobis distance was the most appropriate distance measure for this method's fitness function, which was calculated for each individual in the population who had been allocated a class label by the algorithm.

The Mahalanobis distance is a multivariate distance metric that estimates the distance between a point and a distribution in a multivariate environment. There are several uses for this incredibly valuable statistic, including multivariate anomaly detection, classification on severely unbalanced datasets, and one-class classification.

The Mahalanobis distance is calculated as follows [28]:where MD—Mahalanobis distance; x—Vector of a sample in a dataset; C—Covariance matrix of variables in a dataset; m—Vector of the mean of variables in a dataset.

Finally, GA returned the most suitable individual, and it was on the basis of this that the classification model was developed, with SVM functioning as the classification algorithm. The LOOCV approach was utilized to evaluate the performance of the proposed mRMRe-GA technique, which was applied to four microarray datasets in order to study the performance of the proposed mRMRe-GA technique. A significant benefit of LOOCV is its capacity to avoid “overfitting,” which is one of its primary advantages [29]. Only one sample from each iteration was used as the validating sample in the LOOCV technique; the other samples were treated as training samples in the LOOCV method. This procedure was repeated a number of times in order to cover the whole sample area. In this work, the R programming language (version 3.6.1) was utilized for the construction of mRMRe, GA, and statistical analysis of the data, all of which were accomplished using R programming [30]. Several microarray cancer datasets were used to verify that the findings were statistically valid. The model was run on each dataset with the number of input genes and SVM kernels modified correspondingly.

6. Experimental Setup and Results

6.1. Experimental Setup

Specifically, the microarray dataset is represented by the integers N and M, where N and M are the numbers indicating how many rows and columns there are in the dataset, respectively. The levels of gene expression are depicted as dots on the graph. Examples of the samples are represented by rows, genes by columns, and dots reflect the expression value of a gene for the particular sample and experiment represented by the spots. On the basis of four publicly available benchmark microarray gene expression datasets, the proposed mRMRe-GA approach was examined in order to establish its overall effectiveness. These datasets were donated by the ELVIRA Biomedical Dataset Repository, and they were utilized in this investigation. Almost all of the datasets were large and multidimensional, with dimensional scopes ranging from 2000 to 12600 items per dimensional scope on average. On the next page, you will discover information on the dataset that was evaluated for inclusion in the evaluation.

Every single sample in the colon cancer microarray collection, which includes 22 healthy and 40 tumor samples, has 2000 genes. The genes in the microarray dataset are used to identify and describe each sample. According to current estimates, each sample in the DLBCL outcome has 7129 genes in total, with 32 samples from cured patients and 26 samples from malignant patients in total. It is made up of 47 ALL samples and 25 AML samples, respectively. Each sample is distinguished by 7129 genes, all of which can be found in the leukemia dataset as a whole. 102 observations, 52 of which were cancer and 50 of which were healthy, are included inside the prostate cancer dataset. The dataset contains 6033 gene expression profiles, each of which includes a total of 102 observations. This approach, referred to as mRMRe-GA, is a combination of the mRMRe and the GA techniques. A support vector machine (SVM) is used in the development of the final classification model. The kind of kernel parameters that are employed has a significant impact on the performance of SVMs. The many types of kernels that are used in SVM are illustrated in the following diagram:where K is the kernel function defined as K(x, x)=φ(x).φ(x), which transforms nonlinear sample data points to higher dimension space for better predictions and X, X are n dimensional inputs.

The parameters of the genetic algorithm were initialized and represented in Table 1.

Table 1

Genetic Algorithm parameters.

Parameter	Value
Maximum no. of generations	1–100
Population per generation	20
Probability of crossover	0.8
Probability of mutation	0.1

The first parameter is the maximum number of generations, which varies from 1 to 100. The random population of size n was generated during the initial evolution process. So the solution at step t = 0 is {s1(0), s2(0), s3(0),…, s(0)}. At step t, the fitness value of an individual member of the population, f(s(), was computed and based on the fitness value, and probabilities ρ( were assigned to every individual. From the reproducing population, the new population {s1(, s2(, s3(,…, s(} was formed using crossover and mutation operators. Now, set the t-value as t + 1 and return the algorithm to the fitness evaluation step.

The performance study of the proposed mRMRe-GA method was carried out with other existing algorithms. The classification accuracy was calculated against the number of genes and compared with different algorithms. The accuracy was calculated as the ratio between correct decisions and total samples in the given microarray gene expression dataset. It gave the overall accuracy of the classifier. The various performance parameters considered for the analysis of mRMRe-GA method is given in Table 2.

Table 2

Details of performance parameters.

Name of the parameter	Condition		Definition	Explanation
	Positive	Negative
TPR-true positive rate (sensitivity)	TP-true positive	FP-false positive	TP/(TP + FP)	The closer to 1, the better. TPR = 1 when FP = 0.
TNR-true negative rate (specificity)	TN-true negative	FN-false negative	TN/(TN + FN)	The closer to 1, the better. TNR = 1 when FN = 0.
FPR-false positive rate	FP-false positive	TN-true negative	FP/(FP + TN)	The closer to 0, the better. FPR = 0 when FP = 0.
FNR-false negative rate	FN-false negative	TP-true positive	FN/(FN + TP)	The closer to 0, the better. FNR = 0 when FN = 0.

Based on these parameters, the classification accuracy was defined in terms of positives and negatives as

6.2. Results and Discussion

6.2.1. mRMRe

Four microarray benchmark datasets were used to choose the most informative genes, and the SVM classifier was used to categorize the genes. The SVM classifier had the highest accuracy of any classifier examined, while the mRMRe had the lowest accuracy. The SVM was built with the aid of the software e1071 (Statistical Learning Machine) (see below). The LOOCV approach was used to assess the model's overall effectiveness. On the next page, you can see a link between the accuracy of the SVM classifier with different kernel functions and the number of genes that were selected. During the experiment, it was revealed that the RBF kernel outperformed the polynomial kernel in terms of microarray classification accuracy and efficiency [31]. While Nahar and colleagues [5] chose the polynomial kernel as the kernel function for their experiment, they found that it outperformed the RBF kernel on eight of the nine datasets they tested. Specifically, it was discovered that, for high-dimensional datasets, the RBF kernel surpassed the polynomial kernel when cancer classification is nonlinear and that the RBF kernel beat the polynomial kernel when the cancer classification is linear. On the next page, you will discover information on the performance of the SVM classifier when it is used in conjunction with different kernel functions.

Performance of the SVM classifier [32] when employed with genes selected from the mRMRe database is seen in Figure 6. The mRMRe algorithm was used to choose the top 100 informative genes from the initial list of genes for this investigation, which resulted in a total of 1,000 genes. These genes were entered into GA in order to get the most informative collection of genes that could be used to achieve the highest degree of accuracy. According to the findings of this research, the samples were classified using an SVM classifier, and the accuracy of the classifier was determined using the LOOCV approach.

Figure 6

The performance of the SVM classifier with genes selected from mRMRe.

In the majority of situations, the accuracy of the organization increased as the number of selected genes increased; however, in other cases, it decreased as the number of designated genes decreased. When trained on the Leukemia dataset, the classifier reached 100 percent accuracy with just 5 genes, but the accuracy decreased as the number of genes in the dataset increased, according to the results. For prostate cancer, the classifier obtained 100 percent accuracy for the top 70 and 80 genes, but only 99.02 percent accuracy for the top 75 genes, according to the results. According to the findings, the classifier attained the highest accuracy possible for the DLBCL dataset, with 98.28 percent accuracy for the top 15 genes. After 20 genes were added, the rate reduced to 91.38 percent, according to the study. The accuracy of the top 15 genes in the colon dataset was determined to be the highest, at 93.55 percent, according to the findings. Last but not least, the most informative genes were sent into the GA, which was charged with determining the biomarker genes that would most accurately describe the cancer data that had been collected. Table 3 represent the performance comparison of SVM kernel functions within the system.

Table 3

The performance comparison of SVM kernel functions.

Dataset	No. of genes	Accuracy of SVM with different kernel functions
		Linear	Radial basis	Polynomial	Sigmoid
Colon	5	87.10	87.10	75.81	88.71
	10	85.48	90.32	64.52	88.71
	20	88.71	90.32	64.52	88.71
	30	83.87	90.32	64.52	90.32
	40	87.10	90.32	64.52	90.32
	50	83.87	90.32	64.52	90.32
	60	83.87	88.71	64.52	90.32
	70	80.65	88.71	64.52	88.71
	80	82.26	88.71	64.52	87.10
	90	82.26	88.71	64.52	88.71
	100	82.26	88.71	64.52	88.71
DLBCL outcome	5	82.76	81.03	55.17	68.97
	10	86.21	87.93	55.17	82.76
	15	91.38	98.28	55.17	89.66
	20	91.38	91.38	55.17	87.93
	30	84.48	89.66	58.62	87.93
	40	84.48	89.66	55.17	89.66
	50	87.93	94.83	62.07	86.21
	60	87.93	93.10	55.17	89.66
	70	87.93	91.38	55.17	93.10
	80	86.21	91.38	55.17	87.93
	90	89.66	91.38	55.17	87.93
	100	87.93	93.10	55.17	89.66
Leukemia	5	94.44	100	83.33	98.61
	10	94.44	97.22	93.06	97.22
	20	97.22	97.22	91.67	97.22
	30	95.83	94.44	94.44	97.22
	40	98.61	98.61	94.44	95.83
	50	98.61	95.83	94.44	93.06
	60	98.61	98.61	94.44	95.83
	70	98.61	97.22	91.67	98.61
	80	98.61	98.61	90.28	95.83
	90	98.61	97.22	91.67	95.83
	100	98.61	98.61	90.28	97.22
Prostate	5	87.25	87.25	50.98	86.27
	10	83.33	92.16	50.98	91.18
	20	90.20	97.06	50.98	98.04
	30	95.10	97.06	50.98	91.10
	40	93.14	98.04	50.98	98.04
	50	97.06	98.04	50.98	98.04
	60	97.06	97.06	50.98	99.02
	70	99.02	100	88.24	99.02
	80	100	100	94.11	100
	90	98.04	99.02	97.06	99.02
	100	98.04	100	95.10	99.02

6.2.2. MRMRE-GA

B nmRMRe-GA obtains 100 percent accuracy with just three genes selected, while mRMRe achieves a very high accuracy of 93.55 percent with a total of fifteen genes selected (see Table 4). Although it requires a total of ten genes, the GA reaches a supreme level of accuracy of 93 percent, whilst the mRMR-GA obtains a maximum level accuracy of 95 percent with just five genes. The mRMR may be able to attain a maximum efficiency [33] of 85 percent using just five genes. While just 5 genes from the DLBCL outcome dataset are used, mRMRe-GA achieves 100 percent accuracy, whereas mRMRe obtains a maximum accuracy of 98.28 percent when employing a total of 15 genes. Using 40 genes, the GA obtains a maximum accuracy of 90 percent, whereas using 45 genes, the mRMR-GA reaches a maximum accuracy of 90 percent. The mRMR may be able to attain a maximum efficiency of 85 percent using just five genes. For example, whereas in the instance of leukemia, mRMRe-GA delivers 100 percent accuracy with just three selected gene variants, mRMRe provides 100 percent accuracy with five gene variants. The GA and mRMR-GA both give 100 percent accuracy in the case of 15 genes, whereas the mRMR delivers 100 percent accuracy in the case of 45 genes. mRMRe-GA obtains 100 percent accuracy with just 5 genes selected from the Prostate dataset, while mRMRe achieves a maximum of 99.02 percent accuracy with 45 genes selected from the same dataset, according to the researchers. The GA delivers a maximum accuracy of 91.18 percent in the case of 15 genes, while the mRMR-GA provides a maximum accuracy of 96.08 percent in the case of 45 genes. It is possible to attain accuracy of up to 90.20 percent with 50 genes using the mRMR.

Table 4

Description of Microarray datasets.

Name of the dataset	No. of samples	No. of genes	No. of classes
Colon	62	2000	2
DLBCL outcome	58	7129	2
Leukemia	72	7129	2
Prostate	102	12600	2

Figure 7 represents the comparison using genetic algorithm. The various performance measures of the proposed mRMRe-GA method are given in Table 5. It is said that the method has achieved 100 percent organization accurateness for all input images considered in this learning with the minimum amount of selected genes. Similarly, it has achieved 100 percent sensitivity and specificity. The p-value and kappa value indicate the significance of the proposed method.

Figure 7

Comparison of the mRMRe-GA method with other gene selection methods for four microarray datasets. (a) Colon. (b) DLBCL Outcome. (c) Leukemia. (d) Prostate.

Table 5

The presentation procedures of the proposed mRMRe-GA method for four microarray datasets.

Dataset	# Genes	Accuracy (%)	Sensitivity (%)	Specificity (%)	p value	Kappa value
Colon	4	100	100	100	2.542e − 05	1
DLBCL outcome	6	100	100	100	1.209e − 04	1
Leukemia	3	100	100	100	7.874e − 06	1
Prostate	5	100	100	100	2.887e − 08	1

For four microarray datasets, the results of the mRMRe-GA methodology, as well as the results of other cancer classification techniques, are shown in Table 6. In the Colon dataset, the mRMRe-GA methodology achieves 100 percent classification accuracy with four genes, but the COA-HS and GADP techniques achieve 100 percent classification accuracy with five and eight genes in the Colon dataset, respectively When applied to the Leukemia dataset, the mRMRe-GA strategy achieves 100 percent classification accuracy with just three genes, while other studies, with the exception of the AACO method, need more genes in order to obtain the same classification accuracy. The AACO technique also achieves 100 percent accuracy for three genes, which is an impressive feat. For the purposes of testing this technique, outcome datasets from both prostate cancer and DLBCL were employed. The proposed strategy surpassed the existing approaches in both instances, yielding 100 percent classification for 5 and 6 genes, respectively. The COA-HS strategy achieves performance that is equivalent to that of the proposed method for five genes.

Table 6

Comparison of the mRMRe-GA with other methods.

Algorithms	Colon		DLBCL outcome		Leukemia		Prostate
	#Genes	#Genes	#Genes	Accuracy	#Genes	Accuracy	#Genes	Accuracy
mRMRe-GA	4	100	6	100	3	100	5	100
GBC (Alshamlan et al. [13])	10	98.38			4	100
mRMR-ABC (Alshamlan et al. [15])	15	96.77			14	100
Co-ABC (Alshamlan [14])	9	96.77			3	100
COA-HS (Elyasigomari et al. [21])	5	100			6	100	5	100
GA (Peng et al. [16])	12	93.55			6	100
mRMR-GA (Akadi et al. [19])	5	95.61	45	87.93	15	100	50	96.08
PSO (Shen et al. [17])	20	85.48			23	94.44
mRMR-PSO (Abdi et al. [18])	10	90.32			18	100
GA-SVM (Gunavathi and Hemalatha [20])	10	95	10	77.27	10	95.45	10	92.68
AACO (Xiong and Wang [34])	4	96.77			3	100
GADP (Lee and Leu [35])	8	100			5	100
CS (Gunavathi and Premalatha [4])	10	95	10	72.72	10	95.45	10	92.68

7. Conclusion

In this paper, it is proposed that a unique gene selection approach that combines mRMRe and GA be created in order to achieve 100 percent classification accuracy for four microarray datasets while employing the least number of selected genes. Initial gene selection is carried out with the use of the mRMRe gene selection approach in order to identify beneficial genes that have the least degree of redundancy while also being the most relevant to the class label. A genetic algorithm (GA) is used to analyze the retrieved genes. GA uses the Mahalanobis distance as a distance measure, and it calculates the Mahalanobis distance for each chromosome in the population that has been given a class label. It is possible to develop a classification model by applying the SVM classifier, which searches for genes that are highly informative in the categorizing process. A method known as LOOCV is used in order to assess and evaluate the overall performance of the newly developed model. The results of four microarray datasets are compared to those acquired using different approaches in this study. It is proposed that the mRMRe-GA technology exceeds earlier techniques in terms of accuracy and that it gives the most accurate biological interpretations available [36].