Implementation of Liver Disease Prediction Using Machine Learning

Surya, Boggarapu Sai; Singh, Nitesh Kumar; Rekha, Sheeja

doi:10.32628/cseit2172111

Cited by 1 publication

(2 citation statements)

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“…As the Hessian matrix of the negative log partial likelihood is nonnegative and the L 2,1 regularization norm is convex [54], the objective function Eq (11) is guaranteed to be convexity. Furthermore, since the L 2,1 norm is non-smooth [55], the proximal gradient has been employed to optimize the model along with converging at a fast rate Oð1=εÞ [56].…”

Section: Multitask Cox Modelmentioning

confidence: 99%

“…These algorithms assist clinicians in developing effective intervention strategies for patients and optimize the allocation of medical resources. In view of their significance, numerous machine learning methods have been developed for estimating the personalized risk of patients [8][9][10][11][12]. However, developing clinical personalized risk prediction models is challenging due to the uncertain follow-up time of subjects.…”

Section: Introductionmentioning

confidence: 99%

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Personalized prediction for multiple chronic diseases by developing the multi-task Cox learning model

Zhang,

Yang,

Wang

et al. 2023

PLoS Comput Biol

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Personalized prediction of chronic diseases is crucial for reducing the disease burden. However, previous studies on chronic diseases have not adequately considered the relationship between chronic diseases. To explore the patient-wise risk of multiple chronic diseases, we developed a multitask learning Cox (MTL-Cox) model for personalized prediction of nine typical chronic diseases on the UK Biobank dataset. MTL-Cox employs a multitask learning framework to train semiparametric multivariable Cox models. To comprehensively estimate the performance of the MTL-Cox model, we measured it via five commonly used survival analysis metrics: concordance index, area under the curve (AUC), specificity, sensitivity, and Youden index. In addition, we verified the validity of the MTL-Cox model framework in the Weihai physical examination dataset, from Shandong province, China. The MTL-Cox model achieved a statistically significant (p<0.05) improvement in results compared with competing methods in the evaluation metrics of the concordance index, AUC, sensitivity, and Youden index using the paired-sample Wilcoxon signed-rank test. In particular, the MTL-Cox model improved prediction accuracy by up to 12% compared to other models. We also applied the MTL-Cox model to rank the absolute risk of nine chronic diseases in patients on the UK Biobank dataset. This was the first known study to use the multitask learning-based Cox model to predict the personalized risk of the nine chronic diseases. The study can contribute to early screening, personalized risk ranking, and diagnosing of chronic diseases.

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Section: Multitask Cox Modelmentioning

confidence: 99%