อัลกอริธึมการเรียนรู้ของเครื่องสำหรับการทำนายการคัดแยกผู้ป่วย COVID-19
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งานวิจัยนี้มีวัตถุประสงค์เพื่อพัฒนาระบบทำการคัดแยกผู้ป่วยโควิด-19 ในรูปแบบเว็บแอปพลิเคชันโดยใช้เทคนิคการเรียนรู้ของเครื่องในการจำแนกข้อมูลจากนั้นทำการเปรียบเทียบประสิทธิภาพของอัลกอริทึมในการทำนายการคัดแยกผู้ป่วยโควิด-19 และได้ทำการศึกษาข้อมูลผู้ป่วย จำนวน 1,608,923 ราย จากกรมควบคุมโรค ทำการรวบรวมข้อมูล 1 มกราคม 2563 ถึง วันที่ 1 ตุลาคม 2564 โดยใช้อัลกอริทึมการจำแนกข้อมูลทั้งหมด 3 แบบได้แก่ Random Forest, Neural network และ Naive Bayes ซึ่งทำการเปรียบเทียบประสิทธิภาพรูปแบบเทคนิคทำการนายระหว่างคุณลักษณะ การทดสอบประสิทธิภาพตัวแบบทำนายด้วยวิธีการ Split Test คือ การแบ่งข้อมูลด้วยการสุ่มออกเป็น 2 ส่วนคือ Training Data (ร้อยละ 80) และ Test Data (ร้อยละ 20) โดยใช้โปรแกรม Orange Canvas จากนั้นทำการทดลองเพื่อหาผลการทดสอบประสิทธิภาพที่มีค่าความถูกต้องที่สูงที่สุด ผลการวิจัยพบว่า Random Forest มีค่าความถูกต้องเท่ากับ 93.33% Neural network มีค่าความถูกต้องเท่ากับ 92.7% Naive Bayes มีค่าความถูกต้องเท่ากับ 92.1% จากผลการเปรียบเทียบในครั้งนี้จึงนำ Random Forest ที่มีค่าสูงสุดไปใช้ในการพัฒนาโปรแกรมระบบในการคัดแยกผู้ป่วยโควิด-19ได้
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