Artificial Intelligence in Health Care: Predictive Analysis on Diabetes Using Machine Learning Algorithms

Wadhwa, Shruti; Babber, Karuna

Background: The healthcare organizations are producing heaps of data at alarming rate. This data comprises of medical records, genome-omics data, image scan or wearable medico device data that presents immense advantages and challenges at the same time. These ever growing challenges can be surpassed by applying effective artificial intelligence tools. Methods: This paper uses the large volume of multimodal patient data to perform correlations between Body Mass Index, Blood Pressure, Glucose levels, Diabetes Pedigree Function and Skin Thickness of people in different age groups with diabetes. Python and data analytic packages are used to predict diabetes among people. Results: The blood pressure count of diabetic people comes around 75–85 mmHg and sometimes even higher whereas it is in the range of 60–75 mmHg for non-diabetic people. The people with high body mass index and glucose levels of 120–200 mg/dl and more are found to be diabetic as against the lower body mass index with glucose levels of 85–105 mg/dl of normal people. The Diabetes Pedigree Function count of diabetic people has a peak at 0.25 whereas it is 0.125 in case of non-diabetic people. A similar slight difference in values of Age and Skin Thickness has been found for both diabetic and non-diabetic people. Conclusion: Above results indicate a strong relationship between Blood Pressure, BMI and Glucose levels of people with diabetes whereas a moderate correlation has been found between Age, Skin Thickness and Diabetes Pedigree Function count of people with diabetes. Although present analysis attested many of the previous research findings but getting these inferences matched through analytical tools is a sole purpose of this paper.