A new machine learning approach to optimize correlated biomarkers

被引:0
|
作者
Lee, Ya-Hsun [1 ]
Chen, Yi-Hau [2 ]
Guo, Chao-Yu [1 ,3 ]
机构
[1] Natl Yang Ming Chiao Tung Univ, Inst Publ Hlth, Coll Med, Taipei, Taiwan
[2] Acad Sinica, Inst Stat Sci, Taipei, Taiwan
[3] Natl Yang Ming Chiao Tung Univ, Inst Stat, Hsinchu, Taiwan
关键词
Biomarkers combination; diagnosis accuracy; machine learning; statistical boosting; Youden Index; CLASSIFICATION;
D O I
10.1080/03610926.2025.2477289
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
The number of novel biomarkers is booming. However, a simple predictive score is more feasible to evaluate the clinical outcome and provide better accuracy. However, the optimal linear combination of correlated biomarkers demands comprehensive methodological research. This research aims to develop a novel approach for interpretable optimization. This research proposes the gradient boost machine with the Youden Index (GBYI) as the target function. The rationale is that the gradient boost machine demonstrates superior prediction ability and provides excellent interpretations according to the linear model. In addition, the Youden Index could effortlessly estimate the optimal cutoff point of the diagnostic test and evaluate the overall accuracy. Simulation studies evaluate the performance of the GBYI with linear and nonlinear structured datasets. We also demonstrate an application in the Bupa Liver Disease Data, which revealed that our optimal combination of correlated biomarkers shows an improved prediction with higher accuracy. This research proposes a novel machine-learning strategy using the powerful statistical boosting technique of the Youden Index. The new machine could optimize the combination of high-dimensional data and provide attractive interpretable coefficients.
引用
收藏
页数:12
相关论文
共 50 条
  • [41] Machine Learning to Optimize Permanent Magnet Synchronous Machines
    Ma, Zhuoren
    Arteaga, Ryan
    Wang, Muxuan
    Silveira, Christine
    PROCEEDINGS OF 2020 IEEE 2ND INTERNATIONAL CONFERENCE ON CIVIL AVIATION SAFETY AND INFORMATION TECHNOLOGY (ICCASIT), 2020, : 579 - 584
  • [42] Machine learning to optimize additive manufacturing for visible photonics
    Lininger, Andrew
    Aththanayake, Akeshi
    Boyd, Jonathan
    Ali, Omar
    Goel, Madhav
    Jizhe, Yangheng
    Hinczewski, Michael
    Strangi, Giuseppe
    NANOPHOTONICS, 2023, 12 (14) : 2767 - 2778
  • [43] New perspectives in the differential diagnosis of jaw lesions: Machine learning and inflammatory biomarkers
    Committeri, Umberto
    Barone, Simona
    Arena, Antonio
    Fusco, Roberta
    Troise, Stefania
    Tramontano, Sara
    Bonavolonta, Paola
    Abbate, Vincenzo
    Granata, Vincenza
    Elefante, Andrea
    Ugga, Lorenzo
    Giovacchini, Francesco
    Salzano, Giovanni
    Califano, Luigi
    Orabona, Giovanni Dell'Aversana
    JOURNAL OF STOMATOLOGY ORAL AND MAXILLOFACIAL SURGERY, 2024, 125 (04)
  • [44] Machine learning mapping of lattice correlated data
    Kim, Jangho
    Pederiva, Giovanni
    Shindler, Andrea
    PHYSICS LETTERS B, 2024, 856
  • [45] Machine Learning Phases of Strongly Correlated Fermions
    Ch'ng, Kelvin
    Carrasquilla, Juan
    Melko, Roger G.
    Khatami, Ehsan
    PHYSICAL REVIEW X, 2017, 7 (03):
  • [46] A machine learning approach to optimize the performance of a combined solar chimney-photovoltaic thermal power plant
    Salari, Ali
    Shakibi, Hamid
    Alimohammadi, Mahdieh
    Naghdbishi, Ali
    Goodarzi, Shadi
    RENEWABLE ENERGY, 2023, 212 : 717 - 737
  • [47] An interpretable machine learning approach to estimate the influence of inflammation biomarkers on cardiovascular risk assessment
    Roseiro, M.
    Henriques, J.
    Paredes, S.
    Rocha, T.
    Sousa, J.
    COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2023, 230
  • [48] A Network-based Machine Learning Approach for Identifying Biomarkers of Breast Cancer Survivability
    Pham, Huy Q.
    Guba, Jurko
    Gawanmeh, Mousa
    Porter, Lisa A.
    Ngom, Alioune
    ACM-BCB'19: PROCEEDINGS OF THE 10TH ACM INTERNATIONAL CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND HEALTH INFORMATICS, 2019, : 639 - 644
  • [49] A machine learning approach to knee osteoarthritis phenotyping: data from the FNIH Biomarkers Consortium
    Nelson, A. E.
    Fang, F.
    Arbeeva, L.
    Cleveland, R. J.
    Schwartz, T. A.
    Callahan, L. F.
    Marron, J. S.
    Loeser, R. F.
    OSTEOARTHRITIS AND CARTILAGE, 2019, 27 (07) : 994 - 1001
  • [50] Exploring the Potential Imaging Biomarkers for Parkinson's Disease Using Machine Learning Approach
    Mushta, Illia
    Koks, Sulev
    Popov, Anton
    Lysenko, Oleksandr
    BIOENGINEERING-BASEL, 2025, 12 (01):