Detection of Cardiovascular Disease of Patients at an Early Stage Using Machine Learning Algorithms

Abstract

Among the significant causes of death worldwide, cardiovascular diseases (CVD) account for 80 percent of deaths in low- and middle-income countries such as Bangladesh, according to the World Health Organization (WHO). In Bangladesh, the prevalence of HIV/AIDS and the mortality linked with it have climbed considerably over the past few decades. The rising incidence of cardiovascular disease in Bangladesh needs a complete understanding of the epidemiology of CVD risk among the population. Clinical data analysis is a significant concern for someone dealing with cardiovascular illness. When it comes to generating decisions and making predictions from the vast volumes of data generated by the healthcare industry, machine learning (ML) is to be extremely useful. It is proposed in this research to apply a supervised machine learning algorithm to detect cardiovascular disease (CVD) in individuals early on, allowing them to become concerned about their medical status and avert significant illnesses. When it comes to detecting the disease, four different machine learning methods have been used. The dataset of patients was used, and various machine learning methods, including K-nearest neighbors, Random Forest, Decision trees, and XGBoost, were used to make predictions. As a consequence of the tests, the XGBoost method is superior to the other three tactics (73.72 percent). Moreover, for the modified dataset where smoking, alcohol intake, and physical activity are positive, the percentage is 81.14% to show the effect of smoking and alcohol consumption in a physically active person in terms of cardiovascular disease. Furthermore, these strategies have been evaluated regarding their ability to detect early-stage CVD inpatients. This paper examined the Kaggle dataset to observe the trait and suitability to implement the system for primary data collected from Bangladeshi patients.