Mining Health Care Data To Predict Incidence Of Diabetes - Samples

Question: Discuss about the Mining Health Care Data To Predict Incidence Of Diabetes. Answer: Introduction The use of Data mining is evident in the examination of large datasets for extracting concealed and unexplored data patters, knowledge and relationship. This method is conducive in gathering information where the traditional statistical method is not useful. Data mining in healthcare is an emerging concept which has led to better understanding of the medical data. In general, the rapid growing success of the application of the data mining tools is observed in wide range of application such as analysis of organic compounds, financial forecasting, healthcare and weather forecasting. The practice of data mining in health-care centres is ensures better health policy-making and prevention of hospital errors. The application of the data mining concept is further identified with early detection of the disease, disease prevention, detecting fraudulent insurance claims, cost savings and ensuring more value for money. As per the various types of the previous empirical research evidence, the us e of data mining techniques are used for the diagnosis of different diseases. Some of the most evident form of the disease are seen with the diagnosis of diabetes, stroke, cancer, and heart disease (Raghupathi 2016). Motivation The researchers are seen to be motivated by the worldwide increase in the mortality rate due to diabetes worldwide in the last 10 years. The increasing mortality rate due to diabetes every year and huge availability of data has led to extract useful and knowledgeable information by using data mining techniques for assisting the healthcare specialists in the diagnosis of diabetes. Some of the other reasons for the motivation is taken into account with the developing a tool to be embedded in the hospitals management to provide information to the healthcare professionals (Tsai et al. 2014). This is seen in providing suitable treatment and diagnosis of the diabetes associated diseases. This is observed with diagnosis of diabetes disease such as Nave Bayes, Decision Tree, neural network, kernel density, automatically defined groups, bagging algorithm, and support vector machine showing different levels of accuracies (Chaurasia 2017). The application of data mining in the diagnosis and treatment is helpful for the identification of the research plans among diabetes patients to formulate a treatment plan. It is further seen that the hospitals are not seen to provide equal quality in the diabetes related disease. Henceforth, it is important that a suitable research is conducted to Diabetes disease professionals are having sufficient information of the patients data. It is also considered to be useful in analysing the datasets to extract the valuable knowledge. The data mining is considered as an active tool for the analysing the data to extract the useful knowledge. Some of the main form of the data mining techniques are seen with clustering of the task, maintaining classification trees and producing rule based algorithms which will be conducive in producing a set of rules implemented to classify data (Jothi and Husain 2015). Research Questions The research aims to identify and address the following research questions: What are the main present problems in research techniques in identifying diabetes disease diagnosis and treatment procedures? What are gaps in in the research on diabetes disease diagnosis and treatment? How reliable are mining techniques to diabetes disease treatment in controlling and diagnosis of diabetes disease? Which aspect of the prediction of diabetes symptoms are best traced with the application of data mining techniques? Conclusion It has been discerned that data mining is considered as an effective tool for the analysing the data to extract the useful knowledge. Some of the main form of the data mining techniques are seen with clustering of the task, maintaining classification trees and producing rule based algorithms which will be conducive in producing a set of rules implemented to classify data. The final assessment will be able to contribute to the early detection of the disease, disease prevention, detecting fraudulent insurance claims, cost savings and ensuring more value for money. The research will be able to further contribute to the study of diagnosis of diabetes disease such as Nave Bayes, Decision Tree, neural network, kernel density, automatically defined groups, bagging algorithm, and support vector machine showing different levels of accuracies. The main research questions will be addressed with the identification of the research gaps p in identifying diabetes disease diagnosis and treatment pro cedures. In addition to this, the study will be able to state on the reliability of the mining techniques to diabetes disease treatment in controlling and diagnosis of diabetes disease. References Chaurasia, V., 2017. Early prediction of heart diseases using data mining techniques. Jothi, N. and Husain, W., 2015. Data mining in healthcarea review.Procedia Computer Science,72, pp.306-313. Raghupathi, W., 2016. Data mining in healthcare.Healthcare Informatics: Improving Efficiency through Technology, Analytics, and Management, pp.353-372. Tsai, C.W., Lai, C.F., Chiang, M.C. and Yang, L.T., 2014. Data mining for Internet of Things: A survey.IEEE Communications Surveys and Tutorials,16(1), pp.77-97.