Journal Article

Predicting stroke occurrences: a stacked machine learning approach with feature selection and data preprocessing

Sahu, Preeti Padma, Alsubaie, Najah, Burman, Aniket, Alqahtani, Mohammed S., Abbas, Mohamed, Chakraborty, Pritam, Mallik, Saurav, Bandyopadhyay, Anjan, Soufiene, Ben Othman

2024

Overview

Stroke prediction remains a critical area of research in healthcare, aiming to enhance early intervention and patient care strategies. This study investigates the efficacy of machine learning techniques, particularly principal component analysis (PCA) and a stacking ensemble method, for predicting stroke occurrences based on demographic, clinical, and lifestyle factors. We systematically varied PCA components and implemented a stacking model comprising random forest, decision tree, and K-nearest neighbors (KNN).Our findings demonstrate that setting PCA components to 16 optimally enhanced predictive accuracy, achieving a remarkable 98.6% accuracy in stroke prediction. Evaluation metrics underscored the robustness of our approach in handling class imbalance and improving model performance, also comparative analyses against traditional machine learning algorithms such as SVM, logistic regression, and Naive Bayes highlighted the superiority of our proposed method.

• Publisher: BioMed Central,Springer Nature B.V,BMC
• Subject: Accuracy / Algorithms / Bioinformatics / Biomedical and Life Sciences / Brain research / Business metrics / Clinical outcomes / Comparative analysis / Computational Biology/Bioinformatics / Computer Appl. in Life Sciences / Datasets / Decision making / Decision Trees / Deep learning / Feature selection / Female / Healthcare analytics / Humans / Ischemia / Learning algorithms / Life Sciences / Machine Learning / Male / Medical research / Methods / Microarrays / Patients / Precision medicine / Predictions / Predictive modeling / Principal Component Analysis / Principal component analysis (PCA) / Principal components analysis / Regression analysis / Regression models / Risk assessment / Stacking ensemble / Stroke / Stroke prediction / Support vector machines