Abstract
Late prediction is a major health problem for neurological diseases and early prediction is essential to advance patient outcomes and allow timely intervention. Machine learning (ML) advances are enabling doctors to more efficiently and innovatively predict the onset of neurological conditions using complex biomedical data. In this study the assessment of the power of different ML algorithms to predict Parkinson’s disease, epilepsy, and multiple sclerosis is done to evaluate the relative performance and practical applications. In order to determine the effectiveness of ML techniques, a comprehensive review was done on the various ML techniques e.g. decision trees, k-nearest neighbors (KNN) and ensemble methods. Furthermore, the study validates the predictive capabilities of these approaches, using the Gradient Boosting and Support Vector Machines (SVM) for a case study on EEG and for EEG and clinical datasets. The models were evaluated and compared with respect to known key performance metrics such as accuracy, sensitivity and specificity. Results showed that Gradient Boosting performed best, and with an accuracy of 89% it could predict Parkinson’s earlier on in its first stages. In detecting seizure activity, KNN was very successful accounting for an accuracy of 85%, making it a useful tool for epilepsy diagnosis. The study demonstrated robust generalizability across diverse datasets with ensemble methods, broadly applicable to wider populations for neurological disease prediction. Finally, the study demonstrates that machine learning provides a highly flexible and efficient paradigm for making predictions of neurological disease, with potential for early diagnosis and intervention. The results suggest that ML can be a powerful tool to analyze very complex biomedical data, and in turn develop diagnostic tools targeted towards certain neurological disorders. The integration of ML models with real time clinical systems, and the extension of this to other diseases will further improve diagnostic precision and access in clinical practice.