Abstract
The existing diabetes analysis techniques are considering few parameters like age, sex, BMI, insulin, glucose, blood pressure, diabetes pedigree function, pregnancies. But in this paper, we considered in addition to the basic parameters, we also included serum creatinine, potassium, GlasgowComaScale, heart rate/pulse rate, respiration rate, body temperature, low-density lipoprotein (LDL), high-density lipoprotein (HDL), TG (Triglycerides). Our paper includes analysis of Pima Indian diabetes datasets which are available in UC Irvine (UCI) machine learning repository, the data set which was acquired from a hospital in Frankfurt, Germany, and also visited some local hospitals to get Data sets for a diabetes analysis. Since the analysis includes all parameters which cause diabetes, it may also help to detect diseases like heart disease, neuropathy, retinopathy, hearing loss, and dementia. The main aim of this research work is to analyze the datasets by using different machine learning algorithms along with parameter tuning. The proposed research work also analyzes diabetes risk factors and based on the risk factor, it provides suggestions to the patients. The importance of this paper is continuously monitoring the model if model accuracy reduces, it automatically rebuilds the model and suggests the best model. We prepared the Flask API (Application Programming Interface) to consume this model, which is primarily integrated into the front-end. So that if a user calls this API it will display the diabetes status of the patients. We have collected live samples of datasets from apple phone readings and reached many hospitals for live data sets and in each time if the accuracy reduces in the current model it will automatically reload and considers the model that produces better accuracy.
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Acknowledgment
Thanks to Prabhakar Dasari, Vice president and Manager, Info share systems Pvt Ltd, for support and guidance. Throughout my research, Prabhakar sir helped me in various ways like collecting data sets, model selection, and model building.
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Yaganteeswarudu, A., Dasari, P. (2021). Diabetes Analysis and Risk Calculation – Auto Rebuild Model by Using Flask API. In: Chen, J.IZ., Tavares, J.M.R.S., Shakya, S., Iliyasu, A.M. (eds) Image Processing and Capsule Networks. ICIPCN 2020. Advances in Intelligent Systems and Computing, vol 1200. Springer, Cham. https://doi.org/10.1007/978-3-030-51859-2_27
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DOI: https://doi.org/10.1007/978-3-030-51859-2_27
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