Abstract
With increasing change in sedentary life style and food habits, diabetes is greatly becoming a bane. It is becoming very critical disease in India with more than 62 million diabetic individuals currently diagnosed with the disease. It is also a growing issue throughout the world. But with modern techniques for analysis, it is very much possible to predict the disease very early and control it by pervasive care. Data Mining techniques and Predictive Analysis are focussed in the proposed system. When select techniques are used for classification after pre-processing the data, along with attribute selection and, it is found that the performance of the classifiers is good. The system presents correlation and correspondence analysis over the attributes in the dataset, a comparative analysis amid several classifiers under the experimental environment in order to propose an efficient hybrid classifier to provide precise prediction over the disease.
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Acknowledgment
This research work is a part of the All India Council for Technical Education (AICTE), India funded Research Promotion Scheme project titled “Efficient Prediction and Monitoring Tool for Diabetes Patients Using Data Mining and Smart Phone System” with Reference No: 8- 169/RIFD/RPS/POLICY-1/2014-15.
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Poonkuzhali, S., Jeyalakshmi, J., Sreesubha, S. (2018). Diabetes Mellitus Risk Factor Prediction Through Resampling and Cost Analysis on Classifiers. In: Mandal, J., Sinha, D. (eds) Social Transformation – Digital Way. CSI 2018. Communications in Computer and Information Science, vol 836. Springer, Singapore. https://doi.org/10.1007/978-981-13-1343-1_21
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DOI: https://doi.org/10.1007/978-981-13-1343-1_21
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