IT based healthcare industry is succeeding in leaps and bounds. Nowadays, the massive volume and variety of healthcare data is available. Numbers of procedures have been designed for early diagnosis of human disorders. Here, the critical analysis of different machine learning algorithms in early lifestyle based disease diagnosis has been carried out. Authors have used different techniques like naïve bayes, random forest, neural network, k-NN, C4.5, decision tree etc. Depending upon the sample size of data, the rate of accuracy in forecasting diabetes, cardio and cancer lies in 67–100%, 85–100% and 90–98% respectively. With the passage of time nature, volume, variety and veracity have tremendously changed. Therefore, it is intricate to envision the way the big data may influence the social, corporate and health industries. The effective use of Machine Learning (ML) and Prescriptive Big Data Analytics (PBDA) may assist in developing smart and complete healthcare solution for early diagnosis, treatment and prevention of diseases. Therefore, a smart healthcare framework based upon machine learning and prescriptive big data analytics is designed for accurate prediction of lifestyle based human disorders. The use of prescriptive analytics will improve the accuracy of different machine learning techniques in diagnosis of different life style based human disorders. Finally, the novelty lies in the coexistence of big data, data warehouse and machine learning techniques.
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