Health Application for Women using Decision Tree-Based Classifier
Chona B. Sabinay1, Maria Visitacion N. Gumabay2
1Chona B. Sabinay, Biliran Province State University, Biliran, Philippines, Southeast Asia.
2Maria Visitacion N. Gumabay, St. Paul University Philippines, Cagayan, Philippines, Southeast Asia.
Manuscript received on 05 April 2019 | Revised Manuscript received on 14 April 2019 | Manuscript Published on 24 May 2019 | PP: 12-16 | Volume-8 Issue-6S3 April 2019 | Retrieval Number: F10030486S319/19©BEIESP
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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: Using the descriptive and developmental design, the study developed and evaluated an eHealth application for women using decision tree classifiers. It focuses on the development of an eHealth application system using open-access datasets from UCI Machine Learning Repository. This attempts to predict the onset of diabetes and chronic kidney diseases grounding from the generated predictive models. These decision models are created using C4.5, ID3 and CART algorithms with RapidMiner data science platform. Performance metrics are deployed such as accuracy, recall, precision and error rate to compare the reliability of each model. Models incurred the highest assessment are the bases of the developed system following Agile Software Development Life Cycle Model. Easy access to healthcare workers through teleconsultation, diabetes and chronic kidney disease (CKD) online diagnosis, and maternal care videos are possible with this study. The summary of the evaluation showed that the eHealth Application got an overall average weighted mean of 3.98, which is described as high extent. Based on the respondents’ response, the strongest point of the system was its portability, which earned the highest average mean among categories of system evaluation. Thus, the system addresses the shortcomings of healthcare in terms of distance and timeliness of treatment fostering an equal access to healthcare.
Keywords: Classifier, Data Mining, Health, Prediction, Diagnosis.
Scope of the Article: Computer Science and Its Applications