Building Predictive Models for Cardiovascular Health

The increasing prevalence of cardiovascular diseases has rapidly propelled it to emerge as a worldwide concern in recent times. The pathology is responsible for an annual mortality rate of 18.6 million, with forecasts indicating a noteworthy rise to almost 23 million mortalities per year by the year 2030. The diagnosis of different types of heart conditions may be accomplished by various medical tests, but accurately forecasting heart disease in the absence of such testing poses significant challenges. Machine learning has the capability to facilitate the analysis of large volumes of medical data, enabling the discovery of underlying insights that would otherwise remain imperceptible to human observation. The objective of this research endeavor is to examine the use of machine learning methods in the prediction of heart disease utilizing a provided dataset via the application of the Python programming language. This study aims to construct a reliable prediction model using a dataset including 14 distinct features. The findings indicate that throughout the whole of the sample, 45.87% of individuals are afflicted with heart disease. Additionally, individuals in the older age group (between 50 and 60 years) have a higher susceptibility to heart disease. Furthermore, women within the age range of 55–65 years have a heightened susceptibility to developing cardiac disease. The presented approach has the potential to be used in the fundamental preventive strategy of the World Heart Federation and can be used by healthcare professionals as a diagnostic instrument.