Abstract
In this modern era, one of the prime most facilities available to this generation is state-of-the-art health care, and still diabetes has emerged as one the leading chronic disease. Diabetes is a condition which implies the glucose level is more than the inquisitive level on a managed premise. The prime motto of this study is to provide a good classification of diabetes. There are existing methods, which are for the classification of diabetes popularly datasets “Pima Indian Diabetes Dataset.” Here, the proposed work comprises of four phases: In the first stage, a “Localized Diabetes Dataset” has been compiled and collected from Bombay Medical Hall, Upper Bazar Ranchi, India. In the second stage, neural networks has been used as the classification technique on localized diabetes dataset. In the third stage, GA has been used as a feature selection technique through which six features among twelve features have been obtained. Lastly in the fourth stage, neural networks have been used for classification on suitable attributes produced by GA. In this study, the results have been compared with and without GA for used classification technique. It has been concluded in this work that GA is helpful in removing not only significant attributes, deducing the cost and computation time but also enhancing the ROC and accuracy. The utilized strategy may likewise be executed in other medical issues.
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Acknowledgements
The work done by authors fulfills all the ethical terms and conditions. The data used in the research work were selective and anonymous. Confidentiality of personal and medical data of the patients has been maintained in all aspects. The authors would like to thank firstly all the patients of Bombay Medical Hall, Mahabir Chowk, Pyada Toli, Upper Bazar, Ranchi, Jharkhand, India, who gave us information very patiently and then Dr. Vinay Kumar Dhandhenia, Diabetologist; M/s Sneha Verma Dietitian; Linus ji, and remaining all the staff of Bombay Medical Hall, Ranchi, India, who helped us to collect and compile the dataset of diabetes and non-diabetes patients.
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Choubey, D.K., Paul, S., Dhandhania, V.K. (2019). GA_NN: An Intelligent Classification System for Diabetes. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 817. Springer, Singapore. https://doi.org/10.1007/978-981-13-1595-4_2
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DOI: https://doi.org/10.1007/978-981-13-1595-4_2
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