An Analytical Approach for Early Heart Disease Detection and Prevention Leveraging Machine Learning Techniques

Abstract

A global health issue caused by heart failure is long-term morbidity in most patients with the condition. These conditions should be identified as early as possible for a correct diagnosis, especially to enable proper therapeutic approach. This research aims to assess whether ML methods can develop an accurate first-stage diagnostic model in the detection of HF. On the basis of a set of clinical, demographic, and diagnostic data, various supervised learning methods were tested to predict heart failure for each patient. The mathematical foundation of this study includes the use of supervised learning models, each trained to approximate a mapping function f: X->Y, which are symbolized by the vectors X, to the outcomes Y, where Y = {0, 1}, where 0/1 is the absence/presence of heart failure. The diagnostic ability of models will determine their performance according to precision, an accuracy rate, recall, F1 score metric, and AUC-ROC. The main aim of this present study is therefore two folds; To establish how early the cardiac complications can be detected using different machine learning techniques so that preventive action can be taken. It aims at filling this gap through an evaluation of the literature to establish measures of handling imbalanced datasets. The accuracy of heart disease predictions improved with the implementation of seven machine learning techniques: Random forest, gradient boosting, logistic regression, decision tree, support vector machine (SVM), k-nearest neighbors (KNN), and Naive Bayes. In this work, a focus is given to the mathematical and computational aspects of machine learning methods in cases of early diagnosis and intervention of cardiac issues. It affords a sound support within the clinical settings for proactive and accurate healthcare involvement through the operationalization of predictive analytics into clinical decision making.