**Abstract**

Diabetes is a serious threat to universal health that respects neither socioeconomic rank nor nationwide boundaries. Diabetic foot and lower extremities problems, which affect 40 to 60 million people with diabetes universally, are a significant source of morbidity in people with diabetes. Conducting regular screening and risk stratification for at-risk feet can be greatly used for the management of blood glucose levels. Recent studies revealed that qualitative evidence can be attained using temperature variations from the thermogram of the plantar foot. The changes in temperature distribution are vital in the investigation of diabetic foot, which assist in the early detection of foot ulceration. The main objective of this work is to perform statistical analysis of diabetic foot to draw reasonable and accurate inferences. Besides, there is no gold standard method in classifying the plantar thermal images into any particular group. This may be conquered by quantitatively analyzing the temperature distributions in each foot separately. Since, plantar thermal images are colored in nature, certain color statistical features which are statistically more significant are added with the quantitative temperature distribution to develop an efficient machine learning method to prognosticate the likelihood of diabetes in patients with maximum accuracy is explored.

**Keywords:** diabetic foot, statistical analysis, Thermogram, machine learning, kernel mixer model
