Disease Prognosis Via Machine Learning And Prediction

Document Type : Primary Research paper

Authors

1 Academy of Contemporary Islamic Studies (ACIS), UniversitiTeknologi MARA, Terengganu, Dungun Campus, Malaysia

2 Universiti Utara Malaysia, Sintok, Kedah, Malaysia

3 International Medical University, Malaysia

4 Cluster of Education and Social Sciences, Open University Malaysia

Abstract

The revelation of informationfrom clinical datasets is significant so as to make powerful medical determination. The point of data mining is toextricate information from data put away in dataset and produce clear and reasonable depiction of examples. Diabetesis an interminable sickness and a significant general wellbeing challenge around the world. We utilized Weka tool for the analysis diabetes, no-diabetic examination. Out of sixclassification algorithms, four algorithms depict hundred percent accuracy on train and test data. Overall, in this paper we have performed the data mining using classification algorithms. The data set of hba1c test used inthis work is collected from diagnostics and research laboratory LUMHS, Hyderabad. It is observed by performinghba1c test that many patients were prediabetic and there were less number of patients with diabetes as this test is topredict diabetes by which a patient can go back from becoming diabetic in future. From the classificationexperiments it is evident that the male diabetic patients are more as compared to female diabetic patients. In bothclassification experiments, random forest model shows the highest accuracy.
Int. J. of Aquatic Science
Volume 12, Issue 1
International virtual conference on Newer Trends and Innovation in Nanotechnology Materials Science . Science and Technology
March 2021
Pages 93-102

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