An Assessment on Cardiovascular Disease Prediction and Diagnosis using Machine Learning Algorithms

  • Reddy Anuradha
Keywords: cardiovascular, machine learning, heart disease, prediction, classification


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.


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How to Cite
Anuradha, R. (2022). An Assessment on Cardiovascular Disease Prediction and Diagnosis using Machine Learning Algorithms. Asian Journal For Convergence In Technology (AJCT) ISSN -2350-1146, 8(1), 56-60.

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