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Published in:   Vol. 8 Issue 1 Date of Publication:   June 2019

Holistic Forecasting of Onset of Diabetes through Data Mining Techniques

S.Sankaranarayanan,T.Pramananda Perumal

Page(s):   16-19 ISSN:   2278-2397
DOI:   10.20894/IJBI.105.008.001.005 Publisher:   Integrated Intelligent Research (IIR)

Diabetes is one of the modern day diseases that poses serious threat for the affected and is ever challenging for physicians who are involved in its management and control.Type2 diabetes mellitus ranges in exponential rating day by day in its increase. Mere not being aware of the facts and causes that can lead to such state, unawareness about diabetic symptoms and late detection make diabetic condition unmanageable and is really a challenging task to be faced all victims. This paper suggests holistic measures and means by which any common man can get into it to check whether he / she is a would-be victim of Diabetes through simple checking of symptoms that may lead to Diabetic condition, analyses the factual causes of the aforesaid disease. This would certainly make a person to ensure for the locus-centric state of whether of being a diabetic or not. The problem of diagnosing the onset and incidence of Diabetes is addressed more with a data mining approach in mind. As the success of any data mining approach is solely dependant on the underlying dataset upon which learning is manifested and taken for, this paper inspects more on locating prima-facie symptoms of diabetes disorder. A sagacious insight of analyzing the actual causes of diabetes is set and hence a comprehensive set of data for diabetic condition is proposed here. Subjecting this data to data analysis through simple data mining techniques v.i.z., FP-Growth and Apriori would certainly model a holistic inference engine that could help a doctor to be more astute in confirming the diabetic condition of patients. Association rules are also being inducted based on both of these approaches. A heuristic computer aided diagnosis (CAD) system for diabetes can be built upon this.