Mirza Rizwan Sajid1, Noryanti Muhammad2, Roslinazairimah Zakaria1, Ahmad Shahbaz3, Syed Ahmad Chan Bukhari4, Seifedine Kadry5, A Suresh6. 1. Centre for Mathematical Sciences, College of Computing and Applied Sciences, Universiti Malaysia Pahang, 26300, Gambang, Kuantan, Pahang Darul Makmur, Malaysia. 2. Centre for Mathematical Sciences, College of Computing and Applied Sciences, Universiti Malaysia Pahang, 26300, Gambang, Kuantan, Pahang Darul Makmur, Malaysia. noryanti@ump.edu.my. 3. Punjab Institute of Cardiology, Lahore, 54000, Pakistan. 4. Division of Computer Science, Mathematics and Science, Collins College of Professional Studies, St. Johns University, New York, NY, 11439, USA. 5. Faculty of Applied Computing and Technology, Noroff University College, Kristiansand, Norway. 6. Department of Computer Science and Engineering, SRM Institute of Science & Technology, Kattankulathur, Chengalpattu (D.t), 603 203, Tamilnadu, India.
Abstract
BACKGROUND: In the broader healthcare domain, the prediction bears more value than an explanation considering the cost of delays in its services. There are various risk prediction models for cardiovascular diseases (CVDs) in the literature for early risk assessment. However, the substantial increase in CVDs-related mortality is challenging global health systems, especially in developing countries. This situation allows researchers to improve CVDs prediction models using new features and risk computing methods. This study aims to assess nonclinical features that can be easily available in any healthcare systems, in predicting CVDs using advanced and flexible machine learning (ML) algorithms. METHODS: A gender-matched case-control study was conducted in the largest public sector cardiac hospital of Pakistan, and the data of 460 subjects were collected. The dataset comprised of eight nonclinical features. Four supervised ML algorithms were used to train and test the models to predict the CVDs status by considering traditional logistic regression (LR) as the baseline model. The models were validated through the train-test split (70:30) and tenfold cross-validation approaches. RESULTS: Random forest (RF), a nonlinear ML algorithm, performed better than other ML algorithms and LR. The area under the curve (AUC) of RF was 0.851 and 0.853 in the train-test split and tenfold cross-validation approach, respectively. The nonclinical features yielded an admissible accuracy (minimum 71%) through the LR and ML models, exhibiting its predictive capability in risk estimation. CONCLUSION: The satisfactory performance of nonclinical features reveals that these features and flexible computational methodologies can reinforce the existing risk prediction models for better healthcare services.
BACKGROUND: In the broader healthcare domain, the prediction bears more value than an explanation considering the cost of delays in its services. There are various risk prediction models for cardiovascular diseases (CVDs) in the literature for early risk assessment. However, the substantial increase in CVDs-related mortality is challenging global health systems, especially in developing countries. This situation allows researchers to improve CVDs prediction models using new features and risk computing methods. This study aims to assess nonclinical features that can be easily available in any healthcare systems, in predicting CVDs using advanced and flexible machine learning (ML) algorithms. METHODS: A gender-matched case-control study was conducted in the largest public sector cardiac hospital of Pakistan, and the data of 460 subjects were collected. The dataset comprised of eight nonclinical features. Four supervised ML algorithms were used to train and test the models to predict the CVDs status by considering traditional logistic regression (LR) as the baseline model. The models were validated through the train-test split (70:30) and tenfold cross-validation approaches. RESULTS: Random forest (RF), a nonlinear ML algorithm, performed better than other ML algorithms and LR. The area under the curve (AUC) of RF was 0.851 and 0.853 in the train-test split and tenfold cross-validation approach, respectively. The nonclinical features yielded an admissible accuracy (minimum 71%) through the LR and ML models, exhibiting its predictive capability in risk estimation. CONCLUSION: The satisfactory performance of nonclinical features reveals that these features and flexible computational methodologies can reinforce the existing risk prediction models for better healthcare services.
Authors: Donghee Han; Kranthi K Kolli; Heidi Gransar; Ji Hyun Lee; Su-Yeon Choi; Eun Ju Chun; Hae-Won Han; Sung Hak Park; Jidong Sung; Hae Ok Jung; James K Min; Hyuk-Jae Chang Journal: J Cardiovasc Comput Tomogr Date: 2019-09-23
Authors: Alexandros C Dimopoulos; Mara Nikolaidou; Francisco Félix Caballero; Worrawat Engchuan; Albert Sanchez-Niubo; Holger Arndt; José Luis Ayuso-Mateos; Josep Maria Haro; Somnath Chatterji; Ekavi N Georgousopoulou; Christos Pitsavos; Demosthenes B Panagiotakos Journal: BMC Med Res Methodol Date: 2018-12-29 Impact factor: 4.615
Authors: Mirza Rizwan Sajid; Bader A Almehmadi; Waqas Sami; Mansour K Alzahrani; Noryanti Muhammad; Christophe Chesneau; Asif Hanif; Arshad Ali Khan; Ahmad Shahbaz Journal: Int J Environ Res Public Health Date: 2021-11-29 Impact factor: 3.390