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.
Highlights
It of interest to use machine learning [5,6] and especially Semi-supervised learning [7,8] exploiting known classification and clustering techniques to help in the prognosis diabetes (Figure 1)
The described model consists of three steps: (a) Data preprocessing; (b) Clustering and (c) Classification
The possible diabetic occurrence promising attribute values are identified using sub categorization done by Support Vector Machine (SVM) classification
Summary
The disproportionate intake of unhealthy food is linked with diseases like cancer, obesity, etc. Inadequate intake of food causes anemia, mineral deficiency, mal nutrition and other diseases. Diabetes Mellitus [1] is one of the life-threatening diseases, which causes due to dysfunction of the pancreas due to insufficient secretion of insulin irrespective of age. The prognosis of pre diabetes symptoms using known epidemiological data is useful in treatment. The standard data mining algorithms are used to identify, analyze the hidden data patterns and collect relationships in known stored data. It of interest to use machine learning [5,6] and especially Semi-supervised learning [7,8] exploiting known classification and clustering techniques to help in the prognosis diabetes (Figure 1)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
