A Comparative Evaluation of Machine Learning Techniques for Data-Driven Heart Disease Prediction

In recent years, intelligent technologies have significantly contributed to enhancing patient care, reducing healthcare costs, and alleviating workload, particularly in telehealth settings. This research paper introduces a data-driven heart disease recommendation system aimed at evaluating the efficacy and accuracy of algorithms in delivering personalized medical test recommendations for individuals diagnosed with heart disease. By employing a sliding window technique, time series data from patients is processed to extract pertinent features. These features are utilized to train the models, enabling them to predict the patient's condition for the subsequent day. The system incorporates three classifiers: Random Forest, Logistic Regression, and K-Nearest Neighbors. Experimental results demonstrate that the proposed system achieves a re-markable level of accuracy in providing recommendations. Furthermore, it presents a practical solution to mitigate the burden on individuals with heart disease by reducing the necessity for daily medical tests. The conclusive findings affirm the potential of the proposed system as a valuable tool for analyzing medical data, effectively offering accurate and reliable recommendations to patients with chronic heart diseases, thus improving their healthcare decision-making.