Prediction of Diabetes Through Medical Dataset Using ML

Registration ID: IJNRD_190927

Published ID: IJNRD2304172

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Mr. Omprakash B, Ayush Kumar , Harshitha V, Inchara A

Machine Learning, Logistic Regression, PIMA

Data mining is the process of looking at data from multiple perspectives and combining them with desired data. It is about discovering knowledge or knowledge. Among the many software tools for data analysis, data mining is the most widely used. This allows users to evaluate data from multiple perspectives and dimensions, and group and save relationships. Technically, data mining can be thought of as a step to follow in searching for patterns or analyzing relationships between different sources in large datasets. Current developments in data mining and machine learning are improving the conditions of primary health care by improving research in the field of biomedicine. Regular recording is essential. New medical devices and technologies for diagnosis create mixed data and big data. Therefore, to deal with this poor biomedical data, intelligent data mining and machine learning methods are required to generate demand from the collected raw data calculated as medical data mining. In medical records, medical records only look for patterns and associations that can provide important information for an accurate diagnosis. This technology is used in many medicines (medical applications) and helps to improve diagnosis. Accuracy of classification of medical data and estimation of its value are the main tasks/challenges of medical data mining. Better classifications are needed to improve the predictive value of additional clinical data, as misclassifications can lead to poor estimates. When medical information is used only for medical information, the basic and difficult problems are classification and prediction. Artificial neural network (ANN) and logistic regression (LR) are often used to perform these functions. In our presented research, a hybrid data mining model is proposed for classifying and estimating medical data using LR and ANN, a cross-validated model (CVS) and a percentage selection method (FSM). The performance of the proposed hybrid model will be evaluated based on classification accuracy.

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Mr. Omprakash B, Ayush Kumar , Harshitha V, & Inchara A (April-2023). Prediction of Diabetes Through Medical Dataset Using ML. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(4), b504-b510. https://ijnrd.org/papers/IJNRD2304172.pdf

Volume 8 Issue 4, April-2023

Pages : b504-b510

Paper Reg. ID: IJNRD_190927

Published Paper Id: IJNRD2304172

Research Area: Computer Science & Technology 

Author Type: Indian Author

Country: Bangalore, Karnaraka, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2304172.pdf

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