Abstract
The diabetes mellitus disease (DMD) commonly referred as diabetes is a significant public health problem. Predicting the disease at the early stage can save the valuable human resource. Voluminous datasets are available in various medical data repositories in the form of clinical patient records and pathological test reports which can be used for real-world applications to disclose the hidden knowledge. Various data mining (DM) methods can be applied to these datasets, stored in data warehouses for predicting DMD. The aim of this research is to predict diabetes based on some of the DM techniques like classification and clustering. Out of which, classification is one of the most suitable methods for predicting diabetes. In this study, J48 and Naïve Bayesian techniques are used for the early detection of diabetes. This research will help to propose a quicker and more efficient technique for diagnosis of disease, leading to timely and proper treatment of patients. We have also proposed a model and elaborated it step-by-step, in order to make medical practitioner to explore and to understand the discovered rules better. The study also shows the algorithm generated on the dataset collected from college medical hospital as well as from online repository. In the end, an article also outlines how an intelligent diagnostic system works. A clinical trial of this proposed method involves local patients, which is still continuing and requires longer research and experimentation.
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Das, H., Naik, B., Behera, H.S. (2018). Classification of Diabetes Mellitus Disease (DMD): A Data Mining (DM) Approach. In: Pattnaik, P., Rautaray, S., Das, H., Nayak, J. (eds) Progress in Computing, Analytics and Networking. Advances in Intelligent Systems and Computing, vol 710. Springer, Singapore. https://doi.org/10.1007/978-981-10-7871-2_52
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DOI: https://doi.org/10.1007/978-981-10-7871-2_52
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