• Patel Krupal
• Dhaval A. Parikh

Big data and data analytics changed the way we manage, visualize and analyze data. Now days so much of money is spent for medical treatment and cost incurred during stay at hospital is very high. Predicting stays and possibilities for diseases will reduce this cost. Data analytics also create opportunity in healthcare to and insight from all clinical data captured from many sources like electronics device installed at hospitals and from notes generated from nurses and doctors. Solving problem using machine learning require problems domain knowledge, different machine learning algorithms and understanding of statistics. Accuracy of machine learning algorithm varies and depends on many factor like the nature, size and quality of the data. In this paper, we propose a novel method that aims at finding significant features by applying machine learning techniques resulting in improving the accuracy in the prediction of Mortality Prediction. The prediction model is introduced with different combinations of features and several known classification techniques. Many useful reports and predictive results will be generated as an outcomes which will be useful for doctors to diagnose patients.

Machine learning, mortality prediction, feature selection, prediction model, classification Algorithms

"Clinical Data Analytics in Healthcare using Machine Learning - Mortality Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 5, page no.728-733, May 2020, Available :http://www.jetir.org/papers/JETIR2005415.pdf

"Clinical Data Analytics in Healthcare using Machine Learning - Mortality Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 5, page no. pp728-733, May 2020, Available at : http://www.jetir.org/papers/JETIR2005415.pdf