Mapping of Six Sigma to Threshold Based Incremental Clustering Algorithm

Abstract

The increasing concerns for health, what individual consumes has certainly become one of the most crucial factors to be measured. The statistics shows that diabetes is amongst the highest health concerns that are found in all age groups, posing a huge risk to form cardiovascular diseases in the long run. Hence, to overcome or probably to commercialize such complications, food industries are targeting the health cautious group of people to make profits. But the question remains, whether these products are really genuine. As we see, market aisles these days are crammed with variety of Anti-Diabetic food-products including wheat-flour, cooking-oil, milk tetra packs, etc. of varied brands claiming that they can manage normal blood glucose levels of a diabetic patient and/ or everyone. This raises a debate as to whether these Anti-Diabetic products are effectual preventive measures or useful for diabetes cure. Thus, in this paper we propose the DMAIC problem solving approach of Six Sigma powered by Threshold Based Incremental-Clustering Algorithm (TBCA) implemented here that takes into account nutritional composition of these Anti-Diabetic food-products to analyze their Sugar-release-controlling capability. To validate considered phenomenon, the association between Diabetes Mellitus (DM) and Anti-Diabetic products data sets, are examined through Principal Component Analysis (PCA) and TBCA-integrated-DMAIC steps of Six Sigma. The outcome of this study concludes that these products are constructive in regulating the blood glucose spikes of a diabetic patient. Extended learning outcome of this study will be, to add TBCA process as a new layer in DMAIC, so as to achieve distributed machine learning system, with sustainability care.