Advancing Neurological Health_ A Collaborative Ensemble Learning Approach for Enhancing Early Diagnosis of Amyotrophic Lateral Sclerosis (ALS)

In this study, we propose the integration of Support Vector Machines (SVM) into an ensemble learning framework alongside K-Nearest Neighbors (K-NN) and Naive Bayes models to enhance the early diagnosis of Amyotrophic Lateral Sclerosis (ALS). ALS diagnosis is challenging due to its heterogeneous clinical manifestations. Leveraging diverse datasets including genetic data, medical imaging, and clinical records, we train SVM, K-NN, and Naive Bayes models independently. The ensemble approach amalgamates their predictions, harnessing the complementary strengths of each model. Through rigorous performance evaluation encompassing accuracy, precision, recall, and F1-score metrics, we demonstrate the superior diagnostic capabilities of the ensemble. Employing cross-validation ensures the robustness of our model, while fine-tuning hyperparameters optimizes its performance. Integration of SVM into the ensemble framework contributes additional discriminative power, enhancing the accuracy and reliability of ALS diagnosis. Our research underscores the pivotal role of ensemble learning in addressing the complexities of medical diagnosis, offering a notable advancement in ALS diagnostic methodologies. The proposed approach holds promise for improving patient care and setting new standards in clinical practice