ANALYZING and COMPARING Random Forest and K-Nearest Neighbours for Effective Heart Disease Prediction

This study explores the transformative impact of machine learning (ML) algorithms on various sectors, with a particular focus on healthcare and heart disease prediction. This research emphasizes how advanced machine learning techniques can significantly boost early diagnosis and intervention for heart disease, improving patient outcomes. By employing sophisticated algorithms, the study demonstrates the ability to identify early indicators of heart conditions, leading to enhanced patient care and health results. Through the analysis of extensive datasets, ML algorithms can uncover intricate patterns and risk factors, facilitating the development of personalized treatment plans and preventive strategies. In addition to healthcare, the application of ML extends across diverse fields such as finance, retail, and transportation, where it drives predictive analytics, customer service enhancement, inventory management, and optimized routing. The study compares two prominent ML algorithms, Random Forest (RF) and K-Nearest Neighbors (KNN), elucidating their unique attributes and applicability in different scenarios. RF, an ensemble method, is noted for its accuracy and robustness, excelling in handling large datasets with minimal preprocessing. Conversely, KNN, a non-parametric method, stands out for its simplicity and effectiveness in classification tasks, mainly when the classes are well-separated. The comparative analysis, based on precision, recall, and F1 score metrics, demonstrates KNN’s superior performance in the context of the examined dataset, suggesting its efficacy in scenarios where accurate class distinction is crucial. This comprehensive study underscores the versatility and potential of ML algorithms in advancing healthcare and other vital industries, contributing to more efficient and effective outcomes across various domains.