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(PDF) Detection and Prediction of Diabetes Using Data Mining

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Added on  2021-04-17

(PDF) Detection and Prediction of Diabetes Using Data Mining

   Added on 2021-04-17

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Running head: MINING HEALTH CARE DATA TO PREDICT INCIDENCE OF DIABETESMining Health care data to predict incidence of diabetesName of Student:Name of University:Author’s Note:
(PDF) Detection and Prediction of Diabetes Using Data Mining_1
MINING HEALTH CARE DATA TO PREDICT INCIDENCE OF DIABETES1Table of ContentsIntroduction......................................................................................................................................2Motivation........................................................................................................................................2Conclusion.......................................................................................................................................3Reference List..................................................................................................................................5
(PDF) Detection and Prediction of Diabetes Using Data Mining_2
MINING HEALTH CARE DATA TO PREDICT INCIDENCE OF DIABETES2IntroductionThe use of “Data mining” is evident in the examination of large datasets for extractingconcealed and unexplored data patters, knowledge and relationship. This method is conducive ingathering information where the traditional statistical method is not useful. “Data mining inhealthcare” is an emerging concept which has led to better understanding of the medical data. Ingeneral, the rapid growing success of the application of the data mining tools is observed in widerange of application such as “analysis of organic compounds, financial forecasting, healthcareand weather forecasting”. The practice of data mining in “health-care centres” is ensures betterhealth policy-making and “prevention of hospital errors”. The application of the data miningconcept is further identified with “early detection of the disease, disease prevention, detectingfraudulent insurance claims, cost savings and ensuring more value for money”. As per thevarious types of the previous empirical research evidence, the use of “data mining” techniquesare used for the “diagnosis” of different diseases. Some of the most evident form of the diseaseare seen with the diagnosis of “diabetes, stroke, cancer, and heart disease” (Raghupathi 2016). MotivationThe researchers are seen to be motivated by the worldwide increase in the mortality ratedue to diabetes worldwide in the last 10 years. The increasing mortality rate due to diabetesevery year and huge availability of data has led to extract useful and knowledgeable informationby using data mining techniques for assisting the healthcare specialists in the diagnosis ofdiabetes. Some of the other reasons for the motivation is taken into account with the developinga tool to be embedded in the hospitals management to provide information to the healthcareprofessionals (Tsai et al. 2014). This is seen in providing suitable treatment and diagnosis of thediabetes associated diseases. This is observed with diagnosis of diabetes disease such as “NaïveBayes, Decision Tree, neural network, kernel density, automatically defined groups, baggingalgorithm, and support vector machine showing different levels of accuracies” (Chaurasia 2017). The application of “data mining” in the diagnosis and treatment is helpful for theidentification of the research plans among diabetes patients to formulate a treatment plan. It isfurther seen that the hospitals are not seen to provide equal quality in the diabetes related disease.
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