A COMPUTATIONAL INTELLIGENCE TECHNIQUE FOR EFFECTIVE MEDICAL DIAGNOSIS USING DECISION TREE ALGORITHM (original) (raw)
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Decision Tree based Health Prediction System
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International Journal on Electrical Engineering and Informatics, 2021
Hidden and unknown pattern are extracted from large data sets by performing several combinations of techniques from database and machine learning. Data mining plays a significant role for handling a huge amount of data. Data mining deals with heterogeneity, privacy and correctness of data. Moreover, medical data mining is tremendously important research area and significant attempts are made in this area in recent years because inaccuracy in medical data systems may cause seriously disingenuous medical treatments. Medical data sets should be analyzed using suitable mining algorithms. To perform related operations, techniques of data mining have been used in developing medical systems for prediction of diseases through a set of medical data set. This paper reviews state of the art data mining algorithms for predicting different diseases and to analyze the performance of classification techniques i.e. Naive Bayes (NB), J48, REF Tree, Sequential Minimal Optimization (SMO), Multi-Layer Perceptron and Vote on different data sets of different diseases i.e. chronic kidney disease (CKD), heart disease, liver and diabetes. The experimental setup for performance evaluation of various algorithms using disease data sets retrieved from UCI respiratory has been made in WEKA tool. Values of different parameters i.e. correctly classified instances, precision, recall and F-Measure, time taken are analyzed by applying different classification algorithms.
This study aims to identify the potential benefits that data mining can bring to the health sector, using Indonesian Health Insurance company data as case study. The most commonly data mining technique, decision tree, was used to generate the prediction model by visualizing the tree to perform predictive analysis of chronic diseases. All the steps in data mining process have been performed by a data mining tool, named WEKA. Additionally, WEKA also was utilized to evaluate the prediction performance by measuring the accuracy, the specificity and the sensitivity. Among the result found in this study shows some factors that the health insurance can take into account when predicting the treatment cost of a patient.
International Journal of Engineering Research and Technology (IJERT), 2018
https://www.ijert.org/data-mining-technique-in-the-field-of-medical-science-for-determining-heart-disease-decision-tree https://www.ijert.org/research/data-mining-technique-in-the-field-of-medical-science-for-determining-heart-disease-decision-tree-IJERTCONV6IS15064.pdf Heart disease is the leading cause of death among all other diseases, even cancers. Most of the people facing heart disease is on a raise each year. This prompts for its early diagnosis & treatment. Due to lack of resources in the medical field, the prediction of heart disease periodically may be a problem. Utilization of suitable technology support in this regard can prove to be highly beneficial to the medical fraternity & patients. This issue can be resolved by adopting Data mining techniques. This paper intends to adopt Decision tree-a data mining techniques for the effective prediction of Heart disease. It compares the efficiency & accuracy of the two techniques to decide among them the best.
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A Review of A Novel Decision Tree Based Classifier for Accurate Multi Disease Prediction
International Journal For Scientific Research and Development, 2015
Many researchers have worked on the disease prediction systems using the data mining techniques. Some of the systems are for predicting a single disease and some for the predicting the multiple diseases. Still there is scope to improve the efficiency of the disease prediction. In this synopsis, we are presenting a novel classification based disease prediction system. It uses the concept of classification using the decision tree. Our proposed technique uses greedy approach to select the best attribute for construction of the decision tree. The modified information gain is used. The attribute with highest information gain is selected as the root of the tree. The experimental results have shown that the proposed algorithms classify the data sets more accurately and efficiently. In this synopsis, we have also presented an overview of existing data classification algorithms. These algorithms are described more or less on their own. Data Classification is a very popular and computationally expensive task. We have also elaborated the fundamentals of data classification. From a large number of available algorithms that have been developed we will compare the most important ones.