Predictors of progression through the cascade of care to a cure for hepatitis C patients using decision trees and random forests

Nakayama, Jasmine Y.; Ho, Joyce C.; Cartwright, Emily J.; Simpson, Roy L.; Hertzberg, Vicki

doi:10.1016/j.compbiomed.2021.104461

Cited by 20 publications

(12 citation statements)

References 39 publications

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“…The RF is a classification algorithm for supervised machine learning [41] , [61] , [62] , [63] , [64] . As an ensemble method, the RF has good interpretability and a prominent advantage on small datasets.…”

Section: Methodsmentioning

confidence: 99%

IBPred: A sequence-based predictor for identifying ion binding protein in phage

Yuan

Gao

Xie

et al. 2022

Computational and Structural Biotechnology Journal

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Section: Methodsmentioning

confidence: 99%

IBPred: A sequence-based predictor for identifying ion binding protein in phage

Yuan

Gao

Xie

et al. 2022

Computational and Structural Biotechnology Journal

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“…Therefore, it is for the most part accepted that the degree of intra-have HCV heterogeneity develops over the long haul. Because of elements, for ex-ample, particular breadths and negative choice that happen during persistent disease ( 2 , 12 ), fundamental measurements for evaluating hereditary heterogeneity are not ex-act enough for HCV contamination organizing in light of the intricacy of the primary improvement of HCV populaces inside has.…”

Section: Related Workmentioning

confidence: 99%

“…Hepatitis C spreads through contact with the blood of someone who has HCV. This contact may be through ( 2 ):…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Ensemble Learning Model for Prediction of Hepatitis C Disease

Onyema

Dalal

Dhaou

et al. 2022

Front. Public Health

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Machine learning algorithms are excellent techniques to develop prediction models to enhance response and efficiency in the health sector. It is the greatest approach to avoid the spread of hepatitis C, especially injecting drugs, is to avoid these behaviors. Treatments for hepatitis C can cure most patients within 8 to 12 weeks, so being tested is critical. After examining multiple types of machine learning approaches to construct the classification models, we built an AI-based ensemble model for predicting Hepatitis C disease in patients with the capacity to predict advanced fibrosis by integrating clinical data and blood biomarkers. The dataset included a variety of factors related to Hepatitis C disease. The training data set was subjected to three machine-learning approaches and the validated data was then used to evaluate the ensemble learning-based prediction model. The results demonstrated that the proposed ensemble learning model has been observed ad more accurate compared to the existing Machine learning algorithms. The Multi-layer perceptron (MLP) technique was the most precise learning approach (94.1% accuracy). The Bayesian network was the second-most accurate learning algorithm (94.47% accuracy). The accuracy improved to the level of 95.59%. Hepatitis C has a significant frequency globally, and the disease's development can result in irreparable damage to the liver, as well as death. As a result, utilizing AI-based ensemble learning model for its prediction is advantageous in curbing the risks and improving treatment outcome. The study demonstrated that the use of ensemble model presents more precision or accuracy in predicting Hepatitis C disease instead of using individual algorithms. It also shows how an AI-based ensemble model could be used to diagnose Hepatitis C disease with greater accuracy.

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“…Decision trees have been applied with very diverse medical data, such as those related to cancer [ 15 ], hepatitis [ 16 ] or even COVID-19 [ 17 ]. They have also been applied to data associated with pressure ulcers [ 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Decision-Tree-Based Approach for Pressure Ulcer Risk Assessment in Immobilized Patients

Vera-Salmerón

Domínguez-Nogueira

Romero‐Béjar

et al. 2022

IJERPH

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Applications where data mining tools are used in the fields of medicine and nursing are becoming more and more frequent. Among them, decision trees have been applied to different health data, such as those associated with pressure ulcers. Pressure ulcers represent a health problem with a significant impact on the morbidity and mortality of immobilized patients and on the quality of life of affected people and their families. Nurses provide comprehensive care to immobilized patients. This fact results in an increased workload that can be a risk factor for the development of serious health problems. Healthcare work with evidence-based practice with an objective criterion for a nursing professional is an essential addition for the application of preventive measures. In this work, two ways for conducting a pressure ulcer risk assessment based on a decision tree approach are provided. The first way is based on the activity and mobility characteristics of the Braden scale, whilst the second way is based on the activity, mobility and skin moisture characteristics. The results provided in this study endow nursing professionals with a foundation in relation to the use of their experience and objective criteria for quick decision making regarding the risk of a patient to develop a pressure ulcer.

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Predictors of progression through the cascade of care to a cure for hepatitis C patients using decision trees and random forests

Cited by 20 publications

References 39 publications

IBPred: A sequence-based predictor for identifying ion binding protein in phage

IBPred: A sequence-based predictor for identifying ion binding protein in phage

Artificial Intelligence-Based Ensemble Learning Model for Prediction of Hepatitis C Disease

Decision-Tree-Based Approach for Pressure Ulcer Risk Assessment in Immobilized Patients

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