Research Article
Neha Sharma · Komal Paliwal
Journal
International Journal of Digital Applications and Contemporary Research (IJDACR)
ISSN
2319-4863
Volume / Issue
Vol.9 · Issue 2
Published
September 2020
Access
Open Access
Licence
CC BY-NC-SA 4.0
The main focus of the research study was to identify the most suitable & appropriate data mining algorithm used, implemented in current Medical Decision Support Systems, and also to analyze, evaluate the performance and interpret the results obtained by applying on some medical datasets. According to the previous studies and secondary data analysis three algorithms were found to be appropriate these were C4.5 (J48), Multilayer Perceptron and Naïve Bayes. The C4.5 algorithm for building decision trees is implemented in Weka as a classifier called J48. The different datasets were chosen for assessment, these five UCI databases were Kidney disease, Thoracic Surgery, Autistic Spectrum Disorder Screening Children, Statlog (Heart) and Immunotherapy Dataset. For the analysis various performance metrics or measures were utilized which includes percent of correct classifications instances, True/False Positive rates, absolute mean error, relative root squared error & other set of errors, AUC, Precision, Recall, F-measure, true positive, false positive, false negative and true negative values.
Neha Sharma, Komal Paliwal (2020). Performance Evaluation of Data Mining Algorithms used in Medical Decision Support Systems. International Journal of Digital Applications and Contemporary Research (IJDACR), Vol.9, Issue 2. ISSN: 2319-4863.
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