Classification of Diseases Based on the Symptoms Using Machine Learning

Disease classification plays a crucial role in the field of healthcare, aiding in accurate diagnosis and timely treatment. With the advancements in technology, machine learning algorithms have become instrumental in automating disease classification processes. In this study, we explore the application of MATLAB Classification Learner, a powerful tool for building and comparing machine learning models, in disease classification tasks. We conducted an in-depth analysis of disease classification using various algorithms available in MATLAB Classification Learner. The study utilized a diverse and comprehensive dataset comprising clinical parameters, symptoms, and patient history for multiple diseases. Different classification algorithms, such as support vector machines, decision trees, k-nearest neighbors, and ensemble methods, were employed and compared for their efficiency and accuracy in disease prediction. Our results demonstrate the effectiveness of MATLAB Classification Learner in disease classification tasks. The study revealed significant variations in the performance of different algorithms concerning accuracy, sensitivity, specificity, and computational efficiency. The findings highlight the importance of algorithm selection and parameter tuning in achieving optimal disease classification outcomes. Additionally, the study identified specific algorithms that outperformed others in classifying certain diseases, providing valuable insights for targeted applications.