Semi supervised data mining model for the prognosis of pre-diabetic conditions in type 2 Diabetes Mellitus

Abstract

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.

Machine learning for diabetes clinical decision support: a review

“…Clinical decision support system refers to computerized healthcare systems that provide assistance/aid to clinicians, medical staff or patients themselves for the betterment of health and healthcare services, and implemented through modern technologies like AI, ML and data mining (Abhari et al 2019 ; Kavakiotis et al 2017 ; Sumathi and Meganathan 2019 ) by exploiting medical data (Sidey-Gibbons and Sidey-Gibbons 2019 ). When applied for disease prevention and management, clinical decision support using ML involves intelligent data analysis and inference to assist the physicians in clinical decision making (Barakat et al 2010 ) by facilitating individualized risk profile study, tailor-made treatments, prognostic modeling of patient’s health state etc.…”

“…Further, these morbidities themselves are believed to add to diabetes risk. MeTS and cardiovascular disease have also been studied along with diabetes to investigate whether each one of them is responsible for the onset of the other (cause) or is the resultant condition arising from the disease (effect) (Perveen et al 2019 ; Sumathi and Meganathan 2019 ; Choi et al 2019 ). This section presents a discussion of ML-based diabetes prognosis works identified from the literature as follows—Yokota et al ( 2017 ) developed risk scores for predicting Pre-diabetes to diabetes conversion using multivariate logistic regression analysis.…”

Estimation of the Prevalence of Nonalcoholic Fatty Liver Disease in an Adult Population in Northern China Using the Data Mining Approach

“…Data mining is a practical branch of artificial intelligence that provides well defined and useful information on selecting, exploring, and modeling large amounts of data for the discovery of unknown patterns or relationships. 9,10 Data mining includes the use of traditional and non-traditional statistical methods such as logistic regression and decision tree analysis. In this study, mining algorithms were used to study the patterns of NAFLD onset.…”

<p>Estimation of Diabetes in a High-Risk Adult Chinese Population Using J48 Decision Tree Model</p>

“…Data mining is the process of selecting, exploring and modeling large amounts of data in order to discover unknown patterns or relationships that provide well-defined and useful information, and, in general, this technique has developed rapidly in recent years. 13 , 14 Data mining includes the use of traditional and non-traditional statistical methods, such as logistic regression and decision tree analysis, respectively. We had previously applied a predictive model to identify potential type II diabetes with a sensitive and a highly accurate decision-tree approach.…”