Abstract:
Ischemic heart disease (IHD) is one of the leading causes of death worldwide. However, different geographic regions show different variations of the risk factors of this disease based on the different lifestyles of people. This study examines the current IHD condition in southern Bangladesh, a Southeast Asian middle-income country. The main approach to this research is an AI-based proposal of a reduced set of the greatest impact clinical traits that may cause IHD. This approach attempts to reduce IHD morbidity and mortality by early detection of risk factors using the reduced set of clinical data. Demographic, diagnostic, and symptomatic features were considered for analysing this clinical data. Data pre-processing utilizes several machine learning techniques to select significant features and make meaningful interpretations. A proposed voting mechanism ranked the selected 138 features by their impact factor. In this regard, diverse patterns in correlations with variables, including age, sex, career, family history, obesity, etc., were calculated and explained in terms of voting scores. Among the 138 risk factors, three labels were categorized: high-risk, medium-risk, and low-risk features; 19 features were regarded as high, 25 were medium, and 94 were considered low impactful features. This research’s technological methodology and practical goals provide an innovative and resilient framework for addressing IHD, especially in less developed cities and townships of Bangladesh, where the general population’s socio-economic conditions are often unexpected. The data collection, pre-processing, and use of this study’s complete and comprehensive IHD patient dataset is another innovative addition. We believe that other relevant research initiatives will benefit from this work.
