BibTeX:

@article{IJIRSTV3I10027,
     title={Early Diabetes Detection using Machine Learning: A Survey},
     author={Sakshi Gujral},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={3},
     number={10},
     pages={57--62},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV3I10027.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

Machine learning is one of the aspect of artificial intelligence that allows the development of computer systems that have the ability to learn from experiences without being the need of programming it for every instance. Machine learning is dire need of today’s scenario to eliminate human effort as well as come up with higher automation with less errors. This paper focuses on the review of Early Diabetes detection using machine learning techniques and detection of the frequently occurred disorders with it-mainly Diabetic retinopathy and diabetic neuropathy. The data set employed in most of the concerned literature is Pima Indian Diabetic Data Set. Early diabetes detection is significant as it helps to reduce the fatal effects of the diabetes. Various machine learning techniques like artificial neural network, principal component, decision trees, genetic algorithms, Fuzzy logic etc. have been discussed and compared. This paper first introduces the basic notions of diabetes and then describes the various techniques used to detect it. An extensive literature survey is then presented with relevant conclusion and future scopes with analysis have been discussed.

Machine Learning, Fuzzy Logic, Fuzzy C-Means, SVM, GA, PCA, ANN