Comparing Machine Learning Algorithms to Predict Diabetes in Women and Visualize Factors Affecting It the Most—A Step Toward Better Health Care for Women

Author:

Agarwal Arushi,Saxena Ankur

Publisher

Springer Singapore

Reference20 articles.

1. A. Rathore, S. Chauhan, S. Gujral, in Detecting and predicting diabetes using supervised learning: an approach towards better healthcare for women, IGDTUW Kashmiri Gate, Delhi, India

2. O. Chandrakar, J.R. Saini, Development of Indian weighted diabetic risk score (IWDRS) using machine learning techniques for type-2 diabetes. ACM COMPUTE’16, 21–23 Oct 2016

3. A.G. Karegowda, A.S. Manjunath, M.S. Jayaram, Application of genetic algorithm optimized neural network connection weights for medical diagnosis of pima Indians diabetes. Int. J. Soft Comput. (IJSC) 2(2) (2011)

4. V.V. Vijayan, C. Anjali, Prediction and diagnosis of diabetes mellitus—a machine learning approach, in 2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS), Trivandrum, 10–12 Dec 2015

5. V.V. Kamadi, A.R. Allam, S.M. Thummala, A computational intelligence technique for the effective diagnosis of diabetic patients using principal component analysis (PCA) and modified fuzzy SLIQ decision tree approach. Appl. Soft Comput

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