ABSTRACT
The healthcare industry is generating clinical information in the form of big data, which is stored in the form of electronic health records. But the medical industry needs access to appropriate technology to analyze the data effectively, so that it can help in early detection of disease, treatment recommendations, and clinical services to doctors. Machine learning (ML) algorithms are effective in analyzing both structured and unstructured data. ML also plays an important role in classification. Thus, ML algorithms are useful for classifying the patient dataset based on a particular disease. Algorithms, such as Naïve Bayes, Decision Tree, and Random Forest algorithms, are very helpful in predicting the presence of disease. These types of advanced analytics aid the doctors in patient care by providing better information about the patient’s health. Applying ML techniques to the healthcare industry also reduces cost, increasing patient satisfaction. ML algorithms are useful in predicting heart disease, cancer, neurodevelopmental disorders, etc. This chapter covers different case studies, such as the health monitoring system, disease prediction, precision medicine, and clinical support systems for better prediction of diseases, using ML methods to provide proper healthcare treatment for patients.
