[논문]혈액암 인자 유효성 검증과 분류를 위한 진단 예측 알고리즘 성능 비교 분석

정재승; 주현수; 조치현

doi:10.9717/kmms.2022.25.10.1512

혈액암 인자 유효성 검증과 분류를 위한 진단 예측 알고리즘 성능 비교 분석
Comparative Analysis of Diagnostic Prediction Algorithm Performance for Blood Cancer Factor Validation and Classification 원문보기

멀티미디어학회논문지 = Journal of Korea Multimedia Society, v.25 no.10, 2022년, pp.1512 - 1523

정재승 (Korea Institute of Science and Technology, Post-Silicon Semiconductor Institute) , 주현수 (Korea Institute of Science and Technology, Post-Silicon Semiconductor Institute) , 조치현 (Department of Laboratory Medicine, Korea University Ansan Hospital)

Abstract ▼ AI-Helper

Artificial intelligence application in digital health care has been increasing with its development of artificial intelligence. The convergence of the healthcare industry and information and communication technology makes the diagnosis of diseases more simple and comprehensible. From the perspective of medical services, its practice as an initial test and a reference indicator may become widely applicable. Therefore, analyzing the factors that are the basis for existing diagnosis protocols also helps suggest directions using artificial intelligence beyond previous regression and statistical analyses. This paper conducts essential diagnostic prediction learning based on the analysis of blood cancer factors reported previously. Blood cancer diagnosis predictions based on artificial intelligence contribute to successfully achieve more than 90% accuracy and validation of blood cancer factors as an alternative auxiliary approach.

주제어

표/그림 (15)

그림 Fig. 1. Schematic illustration of k-NN classifications. (a) "AML vs. control group" classification and (b) "PCN vs. control group" classification for Hb and NGAL according to k values.
그림 Fig. 2. Schematic diagram of GNBC configuration and classification process.
그림 Fig. 3. Configuration of confusion matrix.
그림 Fig. 4. Confusion matrices of AML diagnostic prediction cases according to different classifier types. (a) k-NN, (b) GNBC, and (c) RF.
그림 Fig. 5. Sample tree of RF used in AML diagnostic prediction (a) with NGAL feature and (b) without NGAL feature.
그림 Fig. 6. Confusion matrices of CML diagnostic prediction cases according to different classifier types. (a) k-NN, (b) GNBC, and (c) RF.
그림 Fig. 7. Sample tree of RF used in CML diagnostic prediction (a) with NGAL feature and (b) without NGAL feature.
그림 Fig. 8. Confusion matrices of MDS diagnostic prediction cases according to different classifier types. (a) k-NN, (b) GNBC, and (c) RF.
그림 Fig. 9. Confusion matrices of MPN diagnostic prediction cases according to different classifier types. (a) k-NN, (b) NBC, and (c) RF.
그림 Fig. 10. Confusion matrices of PCN diagnostic prediction cases according to different classifier types. (a) k-NN, (b) GNBC, and (c) RF.
그림 Fig. 11. Confusion matrices of PCN diagnostic prediction cases by k-NN (a) with NGAL and (b) without NGAL.
표 Table 1. Diagnostic prediction accuracy of each classifier for blood cancer sample data with NGAL (k-fold cross-validation).
표 Table 2. Diagnostic prediction accuracy of each classifier for blood cancer sample data without NGAL (k-fold cross-validation).
그림 Fig. 12. Feature importance for PCN group’s diagnostic prediction by RF.
그림 Fig. 13. Accuracy comparison of blood cancer diagnosis predictions without NGAL feature.

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표제어: PCR

동의어: Packet Collision Rate

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용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

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