Machine Learning Approaches in Medical Data Processing: A Proposal for an Intelligent Stroke Diagnosis System

Abstract

This study proposes an intelligent diagnostic system based on machine learning and deep learning for stroke detection. The use of artificial intelligence (AI) in healthcare is increasing alongside big data analytics and digitalization. Stroke, a prevalent neurological disease worldwide, can have its mortality and disability rates significantly reduced through early diagnosis. The study utilizes the “Stroke Prediction Dataset” from Kaggle, encompassing 4909 individuals. This dataset includes 12 input features such as age, gender, hypertension, heart disease, and lifestyle factors, along with one output feature indicating stroke status. Data preprocessing steps involved filling missing values with the mean, converting categorical data to numerical format using One-Hot Encoding, applying Min-Max Scaling, and addressing class imbalance with SMOTE. Fifteen different machine learning and deep learning algorithms (e.g., Random Forest, Voting Classifier, Histogram Gradient Boosting, SVM, MLP) were evaluated, with performance measured using accuracy, precision, recall, F1-score, and ROC- AUC metrics. The Voting Classifier achieved the highest performance with 98.5% accuracy and an AUC of 0.99. Tree-based models like Random Forest and Histogram Gradient Boosting also demonstrated high accuracy. Hyperparameter optimization was performed using GridSearchCV and RandomizedSearchCV, while early stopping, regularization, and dropout techniques were applied to prevent overfitting. The findings highlight the superiority of ensemble learning methods over traditional approaches in stroke diagnosis. The study underscores the importance of integrating AI-based clinical decision support systems into healthcare and suggests that model performance could be further enhanced with larger datasets in the future.