Research Area: Machine Learning
Healthcare domain is a very prominent research field with rapid technological advancement and increasing data day by day. In order to deal with large volume of healthcare data we need Big Data Analytics which is an emerging approach in Healthcare domain. Millions of patients seek treatments around the globe with various procedure. Analyzing the trends in treatment of patients for diagnosis of a particular disease will help in making informed and efficient decisions to improve the overall quality of healthcare. Machine Learning is a very promising approach which helps in early diagnosis of disease and might help the practitioners in decision making for diagnosis. This paper aims at building a classifier model using WEKA tool to predict diabetes disease by employing Naive Bayes, Support Vector Machine, Random Forest and Simple CART algorithm. The research hopes to recommend the best algorithm based on efficient performance result for the prediction of diabetes disease. Experimental results of each algorithm used on the dataset was evaluated. It is observed that Support Vector Machine performed best in prediction of the disease having maximum accuracy.
Keywords:
Diabetes Disease Prediction
Machine Learning
Big Data
Healthcare
Machine Learning
Deep Learning
Author(s) Name: Ayman Mir; Sudhir N. Dhage
Journal name:
Conferrence name: Fourth International Conference on Computing Communication Control and Automation (ICCUBEA)
Publisher name: IEEE
DOI: 10.1109/ICCUBEA.2018.8697439
Volume Information:
