Automated disease diagnosis and precaution recommender system using supervised machine learning

Similar to many other professions, the medical field has undergone immense automation during the past decade. The complexity and rise of healthcare data led to a surge in artificial intelligence applications. Despite increased automation, such applications lack the desired accuracy and efficiency for healthcare problems. To address the aforementioned issue, this study presents an automatic health care system that can effectively substitute a doctor at an initial stage of diagnosis and help save time by recommending the necessary precautions. The proposed approach comprises two modules where Modul-1 aims at training the machine learning models using the disease symptoms dataset and their corresponding symptoms and precautions. Preprocessing and feature extraction are done as prerequisite steps. In Module-1 several algorithms are applied to the disease dataset such as support vector machine, random forest, extra trees classifier, logistic regression, multinomial naive Bayes, and decision tree. Module-2 interacts with the user (patient) through which the patient can describe the illness symptoms using a microphone. The voice data are transformed into text using the Google speech recognizer. The transformed data is later used with the trained model for disease prediction, as well as, recommending the precautions. The proposed approach achieves an accuracy of 99.9% during the real-time evaluation.