In order to enhance the early diagnosis of Amyotrophic Lateral Sclerosis (ALS), this study investigates an ensemble learning strategy that combines the advantages of K-Nearest Neighbors (K-NN) and Naive Bayes machine learning models. Diagnosing ALS, a neurodegenerative disease with erratic clinical presentations, can be difficult. We train Naive Bayes and K-NN models using a variety of datasets, including genetic data, medical imaging, and clinical records. By combining these models' predictions, the ensemble technique maximizes each one of their unique advantages. Performance evaluation shows the improved diagnostic capabilities of the ensemble in terms of accuracy, precision, recall, and F1-score. Cross-validation makes sure the model is robust, and hyperparameter tweaking makes it work as best it can. Enhancing early ALS diagnosis with the ensemble approach could lead to improved patient care and clinical standards. This research underscores the significance of ensemble learning in complex medical diagnosis tasks and represents a significant advancement in ALS diagnostic methods.

Amyotrophic Lateral Sclerosis(ALS) , K-Nearest Neighbors (K-NN), Naive Bayes , evaluation parmeters accuracy, precision, recall, and F1-score