Advancements in Multiple Sclerosis Disease Classification Through Machine Learning

In recent years, the field of medical diagnosis has witnessed significant advancements due to the application of machine learning algorithms, especially in complex neurological diseases like Multiple Sclerosis (MS). This study delves into the progress made in MS disease classification using various machine learning techniques, including SVM, KNN, DT, RF, NB, and Extra Trees. Through a diverse dataset of clinical and neuroimaging data, this study has systematically compared the performance of these algorithms in identifying different MS subtypes. SVM and Random Forest exhibited the highest accuracy, while KNN and Decision Trees showed competitive performance. Naive Bayes and Extra Trees also demonstrated promising results in specific scenarios. The study discusses the strengths and weaknesses of each approach and explores the interpretability of the models to understand the key features influencing the classification process. These findings contribute to the growing knowledge base of machine learning in MS disease classification and open avenues for more efficient and accurate diagnostic tools, ultimately leading to personalized patient care and improved treatment planning.