• D.Sathyavathi
• Dr. A. MARY SOWJANYA

One of the most critical problems in healthcare is predicting the likelihood of hospital readmission in case of chronic diseases such as diabetes. Finding readmission in primary stage, allows the hospitals to give special care for those patients, and then can reduce the rate of readmission. In this work we have developed a new model using machine learning. Since hospital readmissions increase the healthcare costs and negatively influence hospitals’ reputation, predicting readmissions in early stages allows prompting great attention to patients with high risk of readmission, which in turn leverages the healthcare system and saves healthcare expenditures. Machine learning helps in providing more accurate predictions than current practices. In this work, we try to predict hospital readmission rate of diabetic patients using standard scaler for preprocessing, decision tree for checking train accuracy and test accuracy, random forest for classification, CATboost that deals with categorical feature and XGBoost classifier .A combination of Machine learning and data engineering were found to outperform other machine learning algorithms when employed and evaluated against real life data. We carry out this process by a number of modules that include feature extraction which improves the process of analysis and helps in the formation of an efficient prediction summary.

Classification, Diabetes Readmission, Feature Selection, Healthcare Analytics, Machine Learning

"Predicting Hospital Readmission for Diabetes Patients Using machine learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 11, page no.1006-1012, November-2020, Available :http://www.jetir.org/papers/JETIR2011276.pdf

"Predicting Hospital Readmission for Diabetes Patients Using machine learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 11, page no. pp1006-1012, November-2020, Available at : http://www.jetir.org/papers/JETIR2011276.pdf