Abstract
Data mining (DM) is a new methodology to data investigation and knowledge discovery The goal of data mining is to gain novel and deep insights and of large datasets which can then be used to support decision making. The information gained can be used for applications ranging from business management, production control, and market exploration to emerging design and science exploration and medical data analysis. Data mining techniques can be applied to medicare domain to catalyze and support goals like avoiding clinical diagnostic tests, finding adverse drug reactions, reducing hospital acquired infections, and rooting out fraud. The goal is to derive life rescuing information which assures better health for the society. Data mining techniques can be used to accomplish this, by using effective analytic tools to discover hidden relationships and trends in medicare data. This paper focuses on studying the enduring maladies like kidney maladies, osteoporosis, arthritis from the medicare dataset. The relation among the enduring maladies and the conforming investigative codes are analyzed by using several data mining (DM) techniques. Then, active conclusion on the various diagnosis tests for each of the chronic maladies is defined, by acknowledging its clinical relevance.
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Dominic, V., Bhatnagar, S., Goyal, G. (2019). A Technique to Envisage Investigative Codes for Enduring Maladies Using Data Mining Techniques. In: Fong, S., Akashe, S., Mahalle, P. (eds) Information and Communication Technology for Competitive Strategies. Lecture Notes in Networks and Systems, vol 40. Springer, Singapore. https://doi.org/10.1007/978-981-13-0586-3_25
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DOI: https://doi.org/10.1007/978-981-13-0586-3_25
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