• HIRALKUMAR DILIPBHAI PATEL

The healthcare business deals with a lot of sensitive and enormous volumes of data that must be treated with care. Diabetes Mellitus is a fatal illness that is growing more frequent across the world. Doctors and nurses desire a dependable way to predict whether or not a patient has Diabetes. Using various machine learning techniques, you may examine the data from many perspectives and distil it into useful knowledge. Using data mining techniques on the massive volumes of data that we have access to and can obtain will allow us to learn useful things. The key objective is to discover new patterns and then figure out how to use these patterns to provide significant and useful information to consumers. Diabetes can cause issues with the heart, kidneys, nerves, and vision. It is critical to develop efficient methods for mining diabetes data. It will be taught how to utilise data mining methods and techniques to determine the best techniques and tactics for effectively categorising the Diabetes dataset and detecting valuable patterns. This study included a medical bioinformatics analysis to predict diabetes. Diabetes was identified using the WEKA computer software as a mining tool. The Pima Indian diabetes database was obtained from the UCI repository and then analysed. The dataset was reviewed and assessed in order to construct a sophisticated model that predicts and identifies the diabetes state. The goal of this study is to compare the accuracy of Naive Bayes, Decision Trees, and (KNN) after using the bootstrapping resampling technique.

PREDICTION OF DIABETES MELLITUS USING MACHINE LEARNING TECHNIQUE AND CLINICAL NOTES

"PREDICTION OF DIABETES MELLITUS USING MACHINE LEARNING TECHNIQUE AND CLINICAL NOTES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 10, page no.d486-d492, October-2022, Available :http://www.jetir.org/papers/JETIR2210373.pdf

"PREDICTION OF DIABETES MELLITUS USING MACHINE LEARNING TECHNIQUE AND CLINICAL NOTES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 10, page no. ppd486-d492, October-2022, Available at : http://www.jetir.org/papers/JETIR2210373.pdf