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Diabetic Mellitus is the leading disease in the world irrespective of age and geographical location. It is estimated that 43% of the overall population is affected by the disease. The reasons for the disease include inappropriate diet lifestyle with allied symptoms like obesity. Therefore, the prognosis and diagnosis of the disease are important for adequate combat and care. The prognosis related known symptoms of the disease include incontinence (inability to control urination) and frequent fatigue. Moreover, early prediction of the disease plays an important role in the prognosis of other associated conditions such as heart failure leading to chronic illness. Hence, it is of interest to describe a data mining based prediction model using known features (derived from epidemiological data collected from the public hospital using routine tests) to help in the prognosis of the disease. We used data pre-processing techniques for handling missing values and dimensionality reduction models to improve data quality. The Minimum Description Length principle (MDL) model for discretization (replacing a continuum with a finite set of points) is used to reduce high-level dimensionality of the dataset, which enabled to categorize the dataset into small groups in ordered intervals. Thus, we describe a semi-supervised learning technique (identifies promising attributes using clustering and classification methods) by combining data mining techniques for reasonable accuracy having adequate sensitivity and specificity for further discussion, cross-validation, revaluation, and application. Early prediction of the disease with improved accuracy by analysing specificity ranges in blood pressure and glucose levels will be useful to combat Diabetes Mellitus.

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In recent years, a few popular classifiers and computational tools have been introduced for the prediction of diabetes mellitus. The robustness of these methodologies is to predict and classify the diabetes mellitus. Diabetes is the most fatal disease that affects the life style of humans. More researchers have focusing on diabetes issues to design efficient classifiers for better classification and prediction. This research work is analyzed with two existing classifiers: (i) hybrid prediction model for type 2 diabetes pattern (HPMT2D) and (ii) type 2 diabetes mellitus prediction model (T2DMPM). These two classifiers are implemented in R tool. An efficient classifier must needed to diagnose and treatment well in advance. Therefore, this article proposes an efficient machine learning based pattern prediction technique called diabetes pattern detection technique using tree ensemble clustering classifier (DDTEC) to accomplish this demand. The proposed classifier is implemented in R tool with BioWeka and studied thoroughly to find classification accuracy of the patterns of type 1 diabetes, type 2 diabetes, and gestational diabetes. From the experimental results, it is noticed that the existing classifiers unable to classify and predict the patterns of various diabetes mellitus with better classification accuracy and F‐measure.