Comparison of Machine Learning and Deep Learning Techniques for Stroke Prediction
Abstract
The early diagnosis and management of diseases in medicine have become critically important in today's world. This comparative thesis focuses on evaluating models developed using various machine learning and deep learning techniques (Logistic Regression, Decision Trees, Random Forest, Support Vector Machine, CNN 1-D, LSTM 1-D, BİLSTM 1-D) to determine the risk of stroke. The obtained highest accuracy values are as follows: LR (0.96), DT (0.95), RF (0.95), SVM (0.96), CNN (0.9442), LSTM (0.9442), BİLSTM (0.9442). The study analyzes a dataset containing various clinical parameters (age, gender, hypertension, heart disease, marital status, occupation type, residence type, average glucose level, BMI, and smoking) using the healthcare-dataset-stroke-data/Fedesoriano. This comparative research aims to make a significant contribution to the field of health by evaluating the effectiveness of different machine learning and deep learning models in determining the risk of stroke.
Keywords
“Health, Stroke, Machine learning, Deep learning, Data analysis”
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