An Overview of Cardiac Disease Diagnosis using Machine Learning Algorithms

Abstract

This abstract investigates the use of machine learning algorithms in the detection of cardiac illness, namely Support Vector Machines (SVM), Logistic Regression, K-Nearest Neighbors (KNN), and Decision Trees. Preprocessing and gathering patient data, including demographic information, medical history, and other health markers, is part of the study. Features are selected based on their relevance to heart disease diagnosis, and labeled datasets are employed for training and validation. SVM, with its capacity to find optimal hyperplanes, is employed to discern patterns in the data. Logistic Regression, known for its simplicity and interpretability, aids in probability estimation. KNN is a flexible instance-based algorithm that makes predictions by utilizing nearby data points. Decision trees are used because they may represent intricate linkages and provide clarity in decision-making. The abstract explores how Comprehensible these algorithms are and how that affects the precision with which heart disease is diagnosed. Robust generalization is ensured by model validation approaches like cross-validation. The study also explores continuous monitoring applications, providing ongoing risk assessments and contributing to personalized treatment plans. The choice of algorithm depends on dataset characteristics and the interpretability requirements of healthcare professionals.