Diabetes Forecasting Using Supervised Learning Techniques

Diabetes Mellitus is one of the most serious health challenges affecting children, adolescents and young adults in both developing and developed countries. To predict hidden patterns of diseases diagnostic in the healthcare sector, nowadays we use various data mining techniques. In this paper, we have applied supervised machine learning techniques like Naive Bayes and J48 decision tree to identify diabetic patients. We evaluated the proposed methods on Pima Indian diabetes data sets, which is a data mining data sets from UCI machine learning laboratory. It has been observed through analysis of the experimental results that Naive Bayes performs better than the decision tree method J48.

[1]  Nickolas Savarimuthu,et al.  Enhancing the Performance of LibSVM Classifier by Kernel F-Score Feature Selection , 2009, IC3.

[2]  Shu-Hsien Liao,et al.  Knowledge management technologies and applications - literature review from 1995 to 2002 , 2003, Expert Syst. Appl..

[3]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[4]  F. Huang,et al.  Breast cancer survivability via AdaBoost algorithms , 2008 .

[5]  G. Magudeeswaran,et al.  Forecast of Diabetes using Modified Radial basis Functional Neural Networks , 2013 .

[6]  Asha Gowda Karegowda,et al.  Cascading K-means Clustering and K-Nearest Neighbor Classifier for Categorization of Diabetic Patients , 2012 .

[7]  Fernando Costa,et al.  Primary Prevention of Cardiovascular Diseases in People With Diabetes Mellitus , 2007, Diabetes Care.

[8]  Rong-Ho Lin,et al.  An intelligent model for liver disease diagnosis , 2009, Artif. Intell. Medicine.

[9]  Teh Ying Wah,et al.  Investigating the Status of Data Mining in Practice , 2003 .

[10]  Ian H. Witten,et al.  Data mining - practical machine learning tools and techniques, Second Edition , 2005, The Morgan Kaufmann series in data management systems.

[11]  Ian Witten,et al.  Data Mining , 2000 .

[12]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[13]  E. Carson,et al.  A probabilistic approach to glucose prediction and insulin dose adjustment: description of metabolic model and pilot evaluation study. , 1994, Computer methods and programs in biomedicine.

[14]  B. ZaitunA..  Investigating the Status of Data Mining in Practice , 2003 .

[15]  D. Feng,et al.  IEEE transactions on information technology in biomedicine: special issue on advances in clinical and health-care knowledge management , 2005 .

[16]  Wei Fan,et al.  Mining big data: current status, and forecast to the future , 2013, SKDD.