Volume 18 , Issue 1 , PP: 182-203, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Lamis F. Al-Qora’n 1 , Qusay Bsoul 2 , Firas Zawaideh 3 , Ala Alzoubi 4 , Silvyras Sayed 5 , Raghad W. Bsoul 6 , Diaa Salama AbdElminaam 7 , Nour Mostafa 8 *
Doi: https://doi.org/10.54216/FPA.180113
In this article, we use machine learning approaches to give a thorough investigation into the prediction of cardiac illnesses and strokes. The Stroke Prediction Dataset and the Heart Failure Prediction Dataset are the two datasets that we use. Our objective is to maximize accuracy and minimize Mean Absolute Error (MAE) and Mean Squared Error (MSE) in order to enhance predictive performance. We use a variety of machine learning methods, such as Random Forests, Naive Bayes, Decision Trees, and k-Nearest Neighbors (KNN). We also use Artificial Neural Networks (ANN) and Multi-Layer Perceptrons (MLP) as deep learning models. We use oversampling approaches to rectify the imbalance in classes. For hyperparameter tweaking, we also use Grid Search and k-Fold Cross Validation. Our goal is to deliver valuable insights into early detection and preventive measures through comprehensive testing and assessment for prevention of strokes and heart diseases.
Heart Disease , Machine learning , Deep learning , Multi layer perceptron , Model evaluation
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