A Comparative Study on Various Machine Learning Techniques for the Prediction of Cardiac Ailment
Keywords:
Heart disease, Machine Learning, Algorithms, Accuracy, PerformanceAbstract
Over the last couple of decades, cardiovascular complexities have become the leading source of death in impoverished regions. With heart attack rates on the acceleration at a youthful age, it is necessary to put in place a process to recognize the symptoms of a heart attack early and thus limit it. It is impossible for a common man to often undergo expensive tests such as an ECG and thus there must be a system that is both efficient and stable to forecast the tendency of heart illness. Early recognition of heart complications can diminish morbidity. However, it is impractical to explicitly supervise patients often and an expert’s consultation is not accessible as it needs knowledge and expertise. In this study we generated and analysed the models for forecasting heart illness, supporting a patient’s heart attributes, and detecting approaching cardiovascular illness using the techniques like Gradient Boost, AdaBoost, CatBoost etc., on a dataset accessible publicly on the Kaggle site, with the results significantly assessed using a confusion matrix. In contrast to other machine learning algorithms, the CatBoost classifier approach has the accuracy of 90.16%, in step with the trial conclusions.
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