Early prediction model for coronary heart disease using genetic algorithms, hyper-parameter optimization and machine lea
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ORIGINAL PAPER
Early prediction model for coronary heart disease using genetic algorithms, hyper‑parameter optimization and machine learning techniques Priya R. L1 · S. Vinila Jinny1 · Yash Vijay Mate2 Received: 22 July 2020 / Accepted: 5 November 2020 © IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract 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. Keywords Genetic algorithm · Evolutionary algorithms · Hyperparameter tuning · Machine learning · Coronary heart disease · Feature selection · Ensemble techniques · Boosting · SMOTE · Optimization · Binary classification · Random forest · Optimized pipeline · TPOT · AutoML · Extreme gradient boosting · Cardiac arrest · Heart attack · Early detection · AI in healthcare
1 Introduction Based on World Health Statistics—2020 report [1] nearly 71% of worldwide mortality happens due to Non Communicable diseases (NCD). There are four major NCD diseases reported for a major rise in mortality rate. Among these, cardiovascular disease (nearly 17.9 million) and chronic respiratory diseases (nearly 3.8 million) continued to be the * Priya R. L [email protected] S. Vinila Jinny [email protected] Yash Vijay Mate [email protected] 1
Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, India
Department of Computer Engineering, Vivekanand Education Society’s Institute of Technology, Mumbai, India
2
main causes for the rise in mortality rate. Also, the study measures the age distribution for dying due to cardiovascular and chronic respiratory disease is between 30 and 70 years. Among all heart diseases, coronary heart disease (CHD), also known as Ischaemic Heart disease is one of the most fatal and challenging factors to prevent any healthy patients. According to the latest study [3], premature mortality in India increased to 59% due to cardiovascular disease and became one of the leading cause
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