Early prediction model for coronary heart disease using genetic algorithms, hyper-parameter optimization and machine learning techniques

Coronary Heart Disease (CHD) is one of the major causes of morbidity and mortality worldwide. According to the World Health Organization (WHO) survey, Cardiac arrest accounts for more deaths annually than any other cause. But the silver lining over here is that heart related diseases are highly preventable, if simple lifestyle modifications are carried out. However, it is a challenging factor to identify high risk heart patients at times due to other comorbidity factors such as diabetes, high blood pressure, high cholesterol and so on. Hence it is needed to develop an efficient early prediction model which can detect high risk patients and their life could be saved. The proposed system helps to identify the best set of features for diagnosis using traditional machine learning algorithms along with modern Gradient Boosting approaches. Genetic algorithm for feature selection to optimize performance by reducing the number of parameters by 20% whilst keeping the accuracy of the model intact is implemented in the proposed system. In addition, hyper parameter optimization techniques are executed to further improve the predictive model's performance.

Automated summary

Coronary Heart Disease (CHD) is a major cause of morbidity and mortality worldwide, but can be prevented through simple lifestyle modifications. Identifying high-risk heart patients can be challenging due to comorbidity factors. Therefore, developing an effective early prediction model is crucial.