การเปรียบเทียบเครื่องจักรเรียนรู้เอ็กซ์ตรีมเพื่อช่วยในการทำนายการเสียชีวิตและการเจ็บป่วยของมารดาตั้งครรภ์ที่มีความไม่สมดุลของข้อมูล
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บทคัดย่อ
This paper aims to find algorithm to predict the maternal mortality and morbidity cases. Cases of maternal mortality and morbidity data from WHO global survey 2007-2008 which is imbalanced data. This imbalanced data is the subject of challenge in in data mining for researchers, called class imbalanced learning (CIL) and lead to difficulties in machine learning and reduce the efficiency of classifiers. Therefore, we have compared several algorithms to handle the imbalanced data classification problem using the synthetic minority hybrid over-sampling and over-sampling technique (SMOTE+TL) used to drive classification models of C4.5, MLP, Naïve Bayes, and the several kinds of Extreme Learning Machine (ELM) as follows: ELM, Circular ELM (CELM), q-Gaussian Extreme Learning Machine (QELM) and q-Gaussian CELM (QCELM) to classify the maternal mortality and morbidity results using Accuracy and G-mean to evaluated performance. Our results demonstrated that ELM show that the ELM algorithm outperformed the other algorithms in Accuracy but QELM outperformed other algorithm by G-mean. This may be helpful performing clinical assessments.
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บทความที่ได้รับการตีพิมพ์เป็นลิขสิทธิ์ของ วารสารวิทยาศาสตร์และเทคโนโลยี มหาวิทยาลัยอุบลราชธานี
ข้อความที่ปรากฏในบทความแต่ละเรื่องในวารสารวิชาการเล่มนี้เป็นความคิดเห็นส่วนตัวของผู้เขียนแต่ละท่านไม่เกี่ยวข้องกับมหาวิทยาลัยอุบลราชธานี และคณาจารย์ท่านอื่นๆในมหาวิทยาลัยฯ แต่อย่างใด ความรับผิดชอบองค์ประกอบทั้งหมดของบทความแต่ละเรื่องเป็นของผู้เขียนแต่ละท่าน หากมีความผิดพลาดใดๆ ผู้เขียนแต่ละท่านจะรับผิดชอบบทความของตนเองแต่ผู้เดียว
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