Monitoringand Detecting Disease in Human Adults Using Fuzzy Decision Tree and Random Forest Algorithm
R. Dhanalakshimi1, C. Geetha2, T. Sethukarasi3

1R. Dhanalakshimi, Assistant Professor, Department of CSE, R.M.K. Engineering College, Gummidipoondi (Tamil Nadu), India.
2C. Geetha, Associate Professor, Department of CSE, R.M.K. Engineering College Chennai (Tamil Nadu), India.
3T. Sethukarasi, Professor, Department of CSE, R.M.K. Engineering College, Gummidipoondi (Tamil Nadu), India.
Manuscript received on 05 February 2019 | Revised Manuscript received on 18 February 2019 | Manuscript Published on 04 March 2019 | PP: 93-99 | Volume-7 Issue-5S2 January 2019 | Retrieval Number: ES2012017519/19©BEIESP
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Abstract: The traditional healthcare involves clinical diagnosis using doctor’s expertise and knowledge. It is a challenge to provide proper healthcare in rural and remote areas since they are more likely to travel a long distance to access specialist diagnosis. The number of medical practitioners and facilities are low in these areas making it difficult to provide an expert diagnosis in a significant time interval. The problem can be solved by delivering expert systems to diagnose disease which is built using data mining method and fuzzy logic. The decision trees are widely used in machine learning to predict results. These medical data and expert decision are best represented as the fuzzy data set. The fuzzy decision trees treat fuzzy data and produce simple decision trees. In this project, we built an expert system that diagnoses disease using the random forest algorithm. The fuzzy decision trees are used to increase the accuracy of the diagnosis system. Thus we use Hybrid Fuzzy Decision tree in Random forest algorithm to identify the disease by analyzing the medical records of the patient in this paper.
Keywords: Random Forest, Fuzzy Decision Trees, Health Care, Diagnosis System.
Scope of the Article: Fuzzy Logics