In this study, a smart risk prediction tool has been demonstrated along with the algorithm, which works as a backend of the tool to detect Type-1 Diabetes. The algorithm was contrived by the weightage values that are articulated by analyzing the risk factors of Type-1 diabetes. The analysis takes place with a machine learning and statistical approach. Data were collected from a number of cases and control groups, which was preprocessed to be fit for the analysis. Risk factors were extracted by

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... omparing two different approaches one is machine learning, and another is the statistical approach. A common regulatory pattern was found that leads to the design of an algorithm that gives a predictive result of the risk level of any user for Type-1 Diabetes. Elaborated results of different approaches have also been shown in this paper, which gives clear excogitation about risk factors and their ranking.