Effective Prediction of Diabetes Mellitus using Nine different Machine Learning Techniques and their Performances
Shashank Joshi1, Vijayendra Gaikwad2, Sairam Rathod3, Anamika Rathod4, Neha Sagar5

1Vijayendra Gaikwad*, Dept. of Computer Engg, VIT, Pune, India.
2Shashank Joshi, Dept. of Computer Engg, VIT, Pune, India. 

Manuscript received on April 11, 2020. | Revised Manuscript received on May 15, 2020. | Manuscript published on June 30, 2020. | PP: 439-445 | Volume-9 Issue-5, June 2020. | Retrieval Number: E9626069520/2020©BEIESP | DOI: 10.35940/ijeat.E9626.069520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Diabetes is a disease where the predominant finding is high blood sugar. The high blood sugar may either be because of deficient insulin production (Type 1) or insulin resistance in peripheral tissue cells (Type 2). Many problems occur if diabetes remains untreated and unidentified. It is additional inventor of various varieties of disorders for example: coronary failure, blindness, urinary organ diseases etc. Nine different machine learning techniques are used in this research work for prediction of diabetes. A dataset of diabetic patient’s is taken and nine different machine learning techniques are applied on the dataset. Positive likelihood ratio, Negative likelihood ratio, Positive predictive value, Negative predictive value, Disease prevalence, Specificity, Precision, Recall, F1-Score ,True positive rate, False positive rate of the applied algorithms is discussed and compared. Diabetes is growing at an increasing in the world and it requires continuous monitoring. To check this we use Logical regression, Random forest, Logical regression CV, Support Vector Machine, Artificial Neural Network (ANN), Decision Tree, k-nearest neighbors (KNN), XGB classifier.
Keywords: Diabetes Prediction, SVM, Random Forest ,Logical Regression, XGB classifier, Accuracy, Precision, Recall, F1-Score, Nine machine learning techniques, Twelve Measures.