A Novel Machine Learning based Stroke Prediction System using Magnetic Resonance Imaging and Adaptive New Fuzzy Inference System - Lakkshmanan, Ajanthaa; Suji R., Adaline; N., Priyanka; Anand, D. Bright; Nagaraju, Komatigunta; Naidu, U. Ganesh (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Smart health analytics is a highly researched field that employs the power and intelligence of technology for efficient treatment and prevention of several diseases. We are currently living in the post COVID phase, which has seen a tremendous rise in sudden deaths caused by many neurological diseases, among which stroke is the major one. It is considered to be the second largest causative disease of death amongst human population according to the World Health Organization. Hence this paper proposes a new method for predicting the onset of stroke using the machine learning approach of Adaptive Neuro Fuzzy Inference System (ANFIS). The input data set for stroke prediction is obtained from Kaggle data repository called as the Brain Stroke prediction dataset which contains 5111 electronic health records of patients with 11 different parameters related to the stroke disease along with brain MRI images. The data obtained is preprocessed using data cleaning methods, segmented using SegNet and features relevant are extracted using CapsuleNet. Predictive analytics is done using ANFIS model and is compared with existing classifiers like Logistic Regression, Random Forest, XG Boost algorithm, Adaboost algorithm and Gated Recurrent Unit. The predictive performance of the proposed model is tested using metrics like accuracy, precision, sensitivity, specificity, F1 measure and ROC curve analysis.