An Assessment on Cardiovascular Disease Prediction and Diagnosis using Machine Learning Algorithms
Abstract
We live in a postmodern era, and our everyday lives are undergoing significant changes that have a beneficial and negative impact on our health. As a result of these developments, the prevalence of numerous diseases has skyrocketed. The diagnosis of cardiovascular disease is the most challenging task in medicine. Cardiovascular disease diagnosis is complex because it relies on the grouping of enormous amounts of clinical and pathological data. As a result of this issue, there has been a substantial surge in interest among researchers and clinical experts in the efficient and precise prediction of cardiac disease. When it comes to heart disease, getting a proper diagnosis at an early stage is crucial because time is a crucial issue. Heart disease is the leading cause of mortality worldwide, and predicting heart disease at an early stage is crucial. In recent years, machine learning has emerged as one of the most progressive, dependable, and supporting tools in the medical arena, providing the most help for disease prediction with proper training and testing. This study work aims to present a survey of knowledge discovery strategies in databases employing data mining techniques that are already in use in medical research, specifically in Cardiovascular Disease Prediction.
References
[6] Mr. Chala Beyene, Pooja Kamat ”Survey on Prediction and Analysis the Occurrence of Heart Disease Using Data Mining Techniques” International Journal of Pure and Applied Mathematics , ijpam Volume 118 No. 8 ,2018, 165-174
[7] P. Suresh and M.D. Ananda Raj ”Study and Analysis of Prediction Model for Heart Disease: An Optimization Approach using Genetic Algorithm” International Journal of Pure and Applied Mathematics , ijpam, Volume 119, No. 16, 2018, 5323-5336
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