Key result
Random forest outperforms other machine learning models with ~90% accuracy for CVD prediction.
Why the study?
Cardiovascular diseases cause high mortality and require precise early diagnosis to improve survival, motivating evaluation of machine learning methods for prediction.
Which machine learning classifier provides the highest accuracy for predicting cardiovascular disease?
Which machine learning classifier provides the highest accuracy for predicting cardiovascular disease?
A Random Forest machine learning model can predict cardiovascular disease with over 90% accuracy, potentially aiding in early detection.
Cardiovascular diseases (CVD) have been a very major reason for deaths over the past few years. There are various factors that are taken under consideration while diagnosing a heart disease and the precision needed is very high. Heart diseases occurs suddenly creating chaos and emergency situations. If the patient gets the treatment on time, there are chances of survival. This paper compares various machine learning approaches for solving this problem based on Image Processing, Sound Signal Processing and various classification algorithms. Though heart disease can be deadly, it’s also preventable in most cases if detected in the early stage and treated properly. In this paper, we have studied and implemented different classifiers like Support vector machine, K nearest neighbor, Random Forest, Decision Tree etc. Random forest classifier is identified as most suitable with accuracy of 90.16% on given dataset. A web application is developed where expert can enter various health parameters of patient and get the prediction of CVD.
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Mrs. Archana Kadam , Shrinivas Patil , Prathmesh Pethkar , Rujul Shikare , Shreyas Sarnayak (2023) studied Cardiovascular diseases (CVD). Random forest classifier vs. Other classifiers (Support vector machine, K nearest neighbor, Decision Tree) was evaluated on Prediction accuracy. A Random forest classifier was identified as the most suitable machine learning approach for predicting cardiovascular disease, achieving an accuracy of 90.16% on the given dataset.
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