GUI BASED PREDICTION OF DIABETIC STAGES ACCURATELY USING MACHINE LEARNING APPROACH

Computer science and Engineering

In 2040, the world’s diabetic patients will reach 642 million, which means that one`s of the ten adults in the future is suffering from diabetes. Diabetes Mellitus (DM) is defined a group of metabolic disorders exerting significant pressure on human health worldwide. DM is a chronic disease characterized by hyperglycaemia and it may cause many complications. To prevent this problem, to analyse the given hospital dataset by supervised machine learning technique (SMLT) while capturing several information’s like variable identification uni-variate analysis, bi-variate analysis, multi-variate analysis, missing value treatments and analyse the data validation, data cleaning/preparing and data visualization. Our analysis provides a comprehensive guide to sensitivity analysis of model parameters with regard to performance in prediction of diabetic patients by given attributes of dataset with evaluation of GUI based user interface diabetes attribute prediction. Additionally, it observes to lead to an increase in the highest accuracy in diabetic prediction of attributes using a better classification report, identifying the confusion matrix and categorizing data from priority and the result shows that the effectiveness of the proposed machine learning algorithm technique can be compared with the best accuracy with precision, Recall and F1 Score.

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