Identification of Effective Factors and Prediction of Ischemic Heart Disease Using Machine Learning Methods and Data from the Yazd Health Study (YaHS) "

Jamal Zarepour Ahmadabadi; Fatemeh Zare Mehrjardi; Mahdieh Ghanbary; Masoud Mirzaei

doi:10.18502/ssu.v32i7.16571

Jamal Zarepour Ahmadabadi Department of Computer Science, Yazd University, Yazd, Iran.
Fatemeh Zare Mehrjardi Department of Computer Engineering, Meybod University, Meybod, Yazd, Iran.
Mahdieh Ghanbary Cardiac Rehabilitation Center, Yazd Cardiovascular Research Center, Non-Communicable Diseases Research Institute, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
Masoud Mirzaei Non-Communicable Diseases Research Institute, Yazd Cardiovascular Research Center, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.

DOI: https://doi.org/10.18502/ssu.v32i7.16571

Keywords: Heart disease, Machine learning, Disease factors, Risk factors.

Abstract

Introduction: Ischemic heart diseases are one of the most common diseases that cause high mortality worldwide. This article has identified various factors affecting heart disease and identified susceptible people using various machine learning methods.

Methods: The current research was conducted on the Yazd Health Study (YaHS) database. YaHS was conducted on adults aged 20-70 years who were residents of Yazd Greater Area and collected information on the health and various diseases of nearly 10,000 people in the form of a questionnaire with 300 different questions. In this research, by using the correlation of questions with heart disease, the most important factors of heart disease have been identified. By using the identified factors and machine learning algorithms, susceptible people with heart disease have been identified.

Results: The results of the evaluations have shown that factors such as age, family history of heart disease, blood pressure, diabetes, blood cholesterol, stress, anxiety, depression, quality of life, quality of sleep, physical activity, smoking, and diet have an effect on heart disease. Likewise, among the different machine learning methods, the nearest neighbor method, the deep neural network method, and the multi-layer perceptron method with a recall criterion of 99.94%, 99.88%, and 99.11% have performed the best in the identifying sick people, respectively.

Conclusion: According to the findings of the research, it can be understood that by controlling factors such as blood pressure, diabetes, blood cholesterol, stress, anxiety, and depression, changing factors such as quality of life, sleep status, physical activity, and eating patterns of people and quitting smoking, it is possible to move towards improving the health of society. On the other hand, the identification of people prone to heart disease using machine learning methods is done faster and at a lower cost than the traditional methods that are done by referring to medical centers and doctors and performing various tests.