Implementing Stacking with Cross - Validation for Heart Disease Prediction.

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The objective of this work is to apply machine learning techniques for the prediction and early identification of cardiovascular disease, a major worldwide health problem. XGBoost, K-nearest neighbors (KNN), decision tree (DT), support vector machine (SVM), and StackingCVClassifier were among the ensemble algorithms used to anticipate cardiac disease using a dataset that came from the UCI ML database. To improve the quality of the data, an exploratory data analysis was performed on the dataset using methods including outlier identification and missing value imputation. Stacking CV Classifier attained the best accuracy rate of 92.20%, according to a comparative examination of pre-processing and post-processing findings. When compared to previous approaches, the suggested strategies performed better in terms of accuracy, recall, and f1-score. Moreover, the flexibility of the model indicates its possible application to other illnesses with comparable features.