Key result
Data standardization and hyperparameter tuning improve the accuracy of machine learning models for cardiac disease prediction.
Why the study?
Manual techniques for diagnosing heart disease are prone to bias and variability, necessitating evaluation of machine learning algorithms for accurate classification.
Does standardizing data and tweaking hyperparameters improve the prediction accuracy of machine learning classifiers for diagnosing heart disease?
Does standardizing data and tweaking hyperparameters improve the prediction accuracy of machine learning classifiers for diagnosing heart disease?
Data standardization and hyperparameter tuning enhance the accuracy of machine learning algorithms in classifying and predicting heart disease.
Should not yet change clinical practice; hypothesis-generating for optimized ML classifiers in cardiac datasets.
Computing power and methodological advancements have brought to a significant deal of variety in the medical sciences, particularly in the area of human cardiac disease diagnosis. It has devastating consequences on human life and is now one of the most severe cardiac disorders in humans. Accurate and timely diagnosis of human cardiac sickness may significantly improve the patient's survival rate and prevent heart failure in its early stages. Manual techniques of diagnosing heart disease are prone to bias and examiner variability. When it comes to identifying and classifying individuals with heart disease from those without, machine learning algorithms are dependable and efficient resources. As per the suggested study, we used the heart disease dataset to analyze the performance of the machine learning algorithms with the help of different metrics, including sensitivity, specificity, F-measure, and classification accuracy. The algorithms were used to determine and forecast human cardiac disease. To do it, we first applied nine different machine learning classifiers to the final dataset (AB, LR, ET, MNB, CART, SVM, LDA, RF, and XGB) and compared their results before and after the change in the hyper parameter. We also do specific pre-processing, dataset standardization, and hyper parameter tweaking to ensure their correctness on the reference dataset for heart disease. We also used the industry-standard ML algorithms are trained and validated using the K-fold cross-validation approach. Finally, the results of the experiment demonstrated that by standardizing data and hyper-tuning the parameters of machine learning classifiers, the prediction classifiers' accuracy increased.
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Dr. Mukesh Choudhary (2026) studied Human cardiac disease. Machine learning classifiers (AB, LR, ET, MNB, CART, SVM, LDA, RF, and XGB) vs. Results before hyperparameter changes was evaluated on Classification accuracy, sensitivity, specificity, and F-measure. Standardizing data and hyper-tuning the parameters of machine learning classifiers increased the accuracy of human cardiac disease prediction.
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