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Author: search.filters.author.Changpetch, Pannapa

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    Item type:Publication,
    Integrating data mining techniques for naïve bayes classification: Applications to medical datasets
    (2021-09-01)
    Changpetch, Pannapa
    ;
    Pitpeng, Apasiri
    ;
    Hiriote, Sasiprapa
    ;
    Yuangyai, Chumpol
    In this study, we designed a framework in which three techniques—classification tree, association rules analysis (ASA), and the naïve bayes classifier—were combined to improve the per-formance of the latter. A classification tree was used to discretize quantitative predictors into cate-gories and ASA was used to generate interactions in a fully realized way, as discretized variables and interactions are key to improving the classification accuracy of the naïve Bayes classifier. We applied our methodology to three medical datasets to demonstrate the efficacy of the proposed method. The results showed that our methodology outperformed the existing techniques for all the illustrated datasets. Although our focus here was on medical datasets, our proposed methodology is equally applicable to datasets in many other areas.
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    Item type:Publication,
    Integrating Spatial-Temporal Risk Factors for an Ambulance Allocation Strategy: A Case Study in Bangkok
    (2018-08-14)
    Chaicharoenwut, Pakinai
    ;
    Koiwanit, Jarotwan
    ;
    Changpetch, Pannapa
    ;
    Buatongkue, Sirisuda
    ;
    Yuangyai, Chumpol
    Dedicated emergency medical services (EMS) are important to patients' chances of survival. In particular, the quicker such services arrive at the scene of an incident, the higher the survival rate. Therefore, the management of ambulance bases is an essential aspect of emergency medical services. Further, the locations of ambulance bases are determined based on patient demand. However, in practice, many elements should be taken into account in a risk assessment of given areas within a locale. Specifically, each area should be assessed for the number and severity of accidents that ordinarily take place there, the number and size of the public events it hosts, its population density, and the number of elderly people resident. In this study, we use a spatial-temporal approach to integrate those factors into a risk assessment of areas relative to each other in a locale. Based on this risk assessment, we determine the optimal locations for ambulance bases in order to minimize response time. We validate our approach using Bangkok as a case study.