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Analysis of Various Diabetes Prediction Techniques Based on Machine Learning

Gurmanvir kaur, Tejbir Singh



Data mining is a method of mine from the underdone data for maintains useful information. In organize to obtain fundamental knowledge it is important to remove bulky quantity of data. This method is consist of many important components like data cleaning, data selection, data integrity, pattern evaluation with data mining engine and database with graphical user interface. Diabetes is a chronic disease that is also known as Non‐Insulin Dependent Diabetes Mellitus, or Adult Onset Diabetes Mellitus. The adequate insulin is created by the patient, which can't be used by body because of absence of affectability to insulin by the cells of the body. The various techniques for the diabetes prediction are reviewed in terms of certain parameters. Many methods of machine learning is used on data mining for primary knowledge like supervised, unsupervised, deep and reinforcement learning techniques. KNN is a part of supervised learning and ANN is an example of deep learning. Neural network is a part of machine learning that can be used for measurement to every disease on human body. If any disease present on human body that indicate in machine. This paper is very important to human body area network. This network is used for analysis to every disease in human body.


Machine Learning, Diabetes prediction, Feature extraction, KNN, ANN, KDD

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