Predicting Diabetes Disease using Random Forest Tree (Rft) Data MiningTechnique
Gul Mohamed Rasitha Banu1, N. Sasikala2, Illham Bashier3, Thani Babikar4
1Dr. Gul Mohamed Rasitha Banu, Department of Health Informatics, FPHTM, Jazan University, KSA.
2Dr. N. Sasikala, Department of Health Informatics, FPHTM, Jazan University, KSA.
3Dr. Illham Bashier, Department of Health Education, FPHTM, Jazan University, KSA.
4Dr. Thani Babikar, Department of Health Education, FPHTM, Jazan University, KSA.
Manuscript received on 18 January 2020 | Revised Manuscript received on 01 February 2020 | Manuscript Published on 05 February 2020 | PP: 46-48 | Volume-8 Issue-4S5 December 2019 | Retrieval Number: D10191284S519/2019©BEIESP | DOI: 10.35940/ijrte.D1019.1284S519
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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 condition that happens when the blood glucose is too high, also known as blood sugar. The primary source of energy is blood sugar, and it comes from the food you eat. Insulin, a pancreatic hormone, helps food glucose get into the cells for energy use. It also leads for an unrelated condition named, “Diabetes Insipidus”, which entails complications with the processing of fluids in the kidney. Insulin is the key to the ability of the cell to use glucose. Problems with the processing of insulin or how cells perceive insulin can easily cause out of control the body’s carefully balanced glucose metabolism process [1]. Diabetes emerges when either of these conditions happens, blood sugar levels rise and crash and the risk of organ damage. Earlier prediction of this diabetes condition could provide proper treatment to protect the people from un avoided illness. For this prediction we can apply data mining which is used predominantly in healthcare organizations for decision making, disease detection purpose. In this paper data have been collected from UCI repositories and the data mining tool (WEKA) is used to predict diabetes. In this database there are 768 instances in which 500 instances belongs to tested negative and 268 instances belongs to tested positive. An experimental study is carried out using data mining technique classification technique called Random Forest Tree (RFT) classifier to predict diabetes. In this research, we have used different cross fold validation to achieve better accuracy and we found that cross fold validation k= 8 gives high accuracy 76.69% while compared with other cross fold validation values.
Keywords: Diabetes, Diabetes Insipidus, Classification, Datamining.
Scope of the Article: Data Mining