Key result
A machine learning model using a support vector machine with improved feature selection achieved 97.5% accuracy in predicting heart disease with a computation time of 4.4 seconds.
Why the study?
Existing techniques for heart disease prediction either take too long to compute or lack accuracy.
Machine learning models, particularly support vector machines combined with feature selection, can predict heart disease with high accuracy and low computational time.
These models merit prospective clinical validation; leaves open integration into cardiac risk stratification.
Coronary heart disease has an intense impact on human life. Medical history-based diagnosis of heart disease has been practiced but deemed unreliable. Machine learning algorithms are more reliable and efficient in classifying, e.g., with or without cardiac disease. Heart disease detection must be precise and accurate to prevent human loss. However, previous research studies have several shortcomings, for example,take enough time to compute while other techniques are quick but not accurate. This research study is conducted to address the existing problem and to construct an accurate machine learning model for predicting heart disease. Our model is evaluated based on five feature selection algorithms and performance assessment matrix such as accuracy, precision, recall, F1-score, MCC, and time complexity parameters. The proposed work has been tested on all of the dataset'sfeatures as well as a subset of them. The reduction of features has an impact on theperformance of classifiers in terms of the evaluation matrix and execution time. Experimental results of the support vector machine, K-nearest neighbor, and logistic regression are 97.5%,95 %, and 93% (accuracy) with reduced computation timesof 4.4, 7.3, and 8seconds respectively.
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Ullah et al. (2022) studied Coronary heart disease. Machine learning models (SVM, KNN, Logistic Regression) with feature selection was evaluated on Model accuracy and computation time. A machine learning model using a support vector machine with improved feature selection achieved 97.5% accuracy in predicting heart disease with a computation time of 4.4 seconds.
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