Machine learning is a growing approach that assists in predicting and diagnosing diseases. In Machine learning, supervised algorithms significantly have remarkable standard systems for disease diagnosis and assist medical experts in the premature detection of high-risk diseases. Machine learning involves two processes training and testing of data sets. Prediction of disease using symptoms of patients and history by applying machine learning algorithms provides highly effective results. Support Vector Machine(SVM), Random Forest(RF), and Logistic Regression(LR) algorithms were the most widely used at prediction due to their accuracy in performance metrics than other algorithms.
Machine Learning algorithms such as Naive Bayes, K-Nearest Neighbor (KNN), and Decision Tree are the other algorithms used to predict the disease. Diabetes, Cancer, Heart disease, chronic kidney disorder, Alzheimer and Parkinson diseases progression, knee osteoarthritis, thyroid, and corona-virus are some of the predicted diseases using machine learning techniques for diagnoses and treatment by the clinicians. Recent advances in disease prediction are chronic disease prediction, automated disease diagnosis, smart healthcare disease diagnosis and monitoring, hybrid intelligent systems for disease prediction.
• In the healthcare field, machine learning methods involve information extraction through medical documents to predict or diagnose a disease.
• Machine learning-based classification and recognition systems capture complex, nonlinear relationships in the data and aid medical experts in the early detection of fatal diseases, and increase the survival rate of patients significantly.
• Machine learning systems can surpass conventional models in illness categorization and prediction of diseases through the cognitive abilities of the human species.
• In addition, machine learning-based computational decision making facilitates patient care, resource allocation, and research on treatments for various diseases are also being performed.
• The lack of effective healthcare analysis tools for identifying hidden information in the patient data leads to misclassifying the results during prediction.
