A Hybridized Machine Learning Based Crisis Period Prediction System for Epileptic Patient Using Crisp-DM and SVM

Abstract

Epileptic seizures are unpredictable and can severely impact the quality of life of patients. To address this challenge, this research presents a hybridized machine learning-based crisis period prediction system designed to predict seizure occurrences with high accuracy. The system leverages a comprehensive dataset that integrates physiological signals, environmental variables, and behavioural patterns, offering a holistic approach to seizure prediction. The methodology follows the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, ensuring a structured approach to data pre-processing, model development, and evaluation. Object-Oriented Analysis and Design Methodology (OOADM) principles were employed to modularize and optimize the implementation, facilitating scalability and maintainability of the system. The dataset underwent rigorous pre-processing, including normalization, feature selection, and handling of missing values, to ensure the quality and reliability of the input data. A Support Vector Machine (SVM) classifier was employed due to its robustness in handling high-dimensional data. The evaluation of the model yielded an accuracy of 85%, demonstrating its effectiveness in predicting crisis periods for epileptic patients. This system represents a significant step forward in predictive healthcare for epilepsy management. By integrating diverse data sources and leveraging advanced machine learning techniques, it offers a promising tool for real-time crisis period prediction, potentially improving patient safety and autonomy. Future work will focus on enhancing the model's accuracy and integrating real-time monitoring capabilities to enable proactive interventions.