A Study of Heart Disease Prediction Using Machine Learning

An accurate categorization of heart disease is essential for cardiologists to deliver effective therapy, making diagnosis and prognosis critical medical jobs. The healthcare sector has significantly boosted its usage of machine learning due to its ability to analyse massive volumes of data and discover patterns. The confluence of machine learning with accessible multimodal data is crucial for future advancement. To reduce the number of fatalities caused by cardiovascular diseases, this work aims to create a model that can reliably predict when these conditions will occur. This study aimed to enhance heart disease detection by investigating and assessing several approaches to early risk assessment. The purpose of this study is to evaluate the efficacy of several machine algorithms to make predictions for this system. From the comparison it is identified that the K-Nearest Neighbour algorithm offers the highest accuracy of 95.60%. The applications highlight the significance of data extraction that generates fresh hypotheses for further study and knowledge of heart disease.