Application of Decision Tree Algorithm for Data Mining in Healthcare Operations: A Case Study

Abstract

By means of data mining techniques, we can exploit furtive and precious information through medicine data bases. Because of huge amount of this information, study and analyses are too difficult. We want some methods to exploring through data and extract valuable information which can be used in the future similar cases. One of these cases is accouchement. The mechanism of accouchement is a natural and spontaneous process without the need to any intervention. In some conditions, maybe mother, baby or both of them are in hazard and need help and support. This help is provided by Caesarian Section which saves mother and baby. Nevertheless, we need to know when we should use surgery. This study explains utilization of medical data mining in determination of medical operation methods. We render this with accumulating 80 pregnant women information. The results show that decision tree algorithm designed for this case study generates correct prediction for more than 86.25% tests cases

An Effect of Machine Learning Based Classification Algorithms on Chronic Kidney Disease

“…Due to the availability of numerous classification algorithms for CKD prediction, there is a requirement to analyze the prediction performance of these algorithms. In this paper, a comparison study is made among four data classification algorithms namely DL [6], DT, RF [7] and RT. The computation of classifying techniques has been examined under the application of standard CKD dataset which is obtained from UCI repository.…”

An Exploration of Various Data Mining Techniques for Application in Child Healthcare

“…We use data mining for treatment of various diseases. Gharehchopogh et al [7] talk about data mining as a process in which new patterns, data sifting, patterns, trends, correlations, etc are done using statistical and mathematical techniques (Gartner Group). The techniques are kd method and decision tree algorithm.…”

Comparison of Data Mining Algorithms in the Diagnosis of Type Ii Diabetes

“…Data mining is a self-knowledge discovery and a process for the analysis of large databases, providing unknown, hidden, meaningful, and useful patterns automatically obtained from largescale databases [6]. Data mining algorithms such as neural networks [6], support vector machines [3], decision tree [7] and ... have been used successfully in various medical fields. These algorithms have been able to be a decent solution in different diseases' diagnostic systems such as diabetes [3], heart disease [6], breast cancer [9], etc.…”

“…These algorithms have been able to be a decent solution in different diseases' diagnostic systems such as diabetes [3], heart disease [6], breast cancer [9], etc. Also, use of data mining algorithms makes new information and relationships embedded in large and complex datasets evident through inference and learning new patterns and relations [7,8]. The use of large amounts of patients' data to diagnose disease, using data mining algorithms, improves the accuracy of these algorithms [7,8].…”

“…Also, use of data mining algorithms makes new information and relationships embedded in large and complex datasets evident through inference and learning new patterns and relations [7,8]. The use of large amounts of patients' data to diagnose disease, using data mining algorithms, improves the accuracy of these algorithms [7,8]. The data used in this article includes768 samples from diabetic patients obtained from Pima Indians Diabetes Data Set.…”