Hepatocellular Carcinoma Detection Using Machine Learning Techniques

Angelis, Ioannis; Exarchos, Themis P.

doi:10.1007/978-3-030-78775-2_4

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…The best results achieved up to 84% accuracy. [41] The k-NN and SVM classifiers were utilized to determine whether the patients have abnormal or normal respiration, or have bradypnea (slow breathing), or tachypnea (fast breathing). The testing accuracies of the completely built SVM and k-NN classifiers were 96% and 99%, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Accurate predicting the development of cancers or complications of cancers could indicate earlier diagnosis and therapeutic approaches that would improve outcomes. [26,31,33,35,37,39,41,53,55,[58][59][60]73] The majority of these ML applications use imaging data (most often histologic type) for classification of malignant versus benign tumors. Cardiovascular conditions and DM are among the most common medical conditions used in predictive analysis.…”

Section: Discussionmentioning

confidence: 99%

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Systematic Review of Supervised Machine Learning Models in Prediction of Medical Conditions

Ljubić

Pavlovski

Gillespie

et al. 2022

Preprint

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Machine learning (ML) models for analyzing medical data are critical for both accelerating development of novel diagnostic and treatment strategies and improving the accuracy of medical care delivery. Our objective was to comprehensively review supervised ML models for diagnosis or treatment prediction. Publications indexed in PubMed were reviewed to identify articles utilizing supervised predictive ML models in medicine. Articles published between 01/01/2020–01/01/2022 were included in this review. Initially, PubMed was searched using MeSH major terms, and if more extensive search results were needed, a broader search was applied (titles/abstracts).PubMed indexed 21,268 published articles (MeSH Major topic) describing ML methods implemented in medicine. Of those, 11,726 articles were published within the last 2 years. Most of the published ML models in medicine in the last two years were different types of deep learning models (about 75%). Fifty articles were included in this review.Almost all categories of disease were subjects of ML predictions. Positive and negative factors in each of the scenarios need to be evaluated before the most optimal ML model is selected. Domain knowledge and collaborations between physicians and ML experts can improve the selection and prediction performance of ML models in medicine and facilitate implementation in clinical practice. Predictive ML models could provide recommendations to recruit suitable patients for clinical trials. Prediction ML models may contribute to development of more effective diagnostic and therapeutic choices, founded on evidence-based medicine. A broad range of methodological approaches have been taken toward this goal, and those approaches are presented here with their various advantages and disadvantages.AUTHOR SUMMARYOver the last decade, there has been rapid development of Machine learning (ML) methods to analyze Big Data in medicine. ML is aimed to make the computer learn from past experiences and make predictions by recognizing patterns in medical data. We performed a comprehensive systematic literature review of recent publications (last two years), indexed in PubMed/MEDLINE that have described either traditional or deep supervised prediction ML models in medicine. We identified 21,268 articles describing ML implementation in medicine. 11,726 articles were published in the last 2 years. We presented the number of publications describing each of the most often ML methods to show current trends in development of these models. Most of the recently published ML models in medicine were deep learning models. We found that the understanding of disease is likely to lead to more accurate prediction. An important dilemma is the selection of optimal ML models for a specific task, considering amount and type of available data. Domain knowledge and collaborations between physicians and ML experts can improve the prediction performance of ML models, which could help clinicians to select the most effective diagnostic and therapeutic choices available and decrease medical errors.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Systematic Review of Supervised Machine Learning Models in Prediction of Medical Conditions

Ljubić

Pavlovski

Gillespie

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…They found that the gradient boosting model had the highest accuracy. Similarly, Angelis et al 16 used six algorithms including decision tree, random forest, SVM, k-nearest neighbor (KNN) classification, AdaBoost, and gradient boosting to make models based on the collected clinical data. They also found that the gradient boosting had the highest accuracy of 84% and a sensitivity of 92%.…”

Section: For the Diagnosis Of Hccmentioning

confidence: 99%

Current Status and Analysis of Machine Learning in Hepatocellular Carcinoma

Feng¹,

Wang²,

Wang³

et al. 2023

J Clin Transl Hepatol

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Hepatocellular carcinoma (HCC) is a common tumor. Although the diagnosis and treatment of HCC have made great progress, the overall prognosis remains poor. As the core component of artificial intelligence, machine learning (ML) has developed rapidly in the past decade. In particular, ML has become widely used in the medical field, and it has helped in the diagnosis and treatment of cancer. Different algorithms of ML have different roles in diagnosis, treatment, and prognosis. This article reviews recent research, explains the application of different ML models in HCC, and provides suggestions for follow-up research.

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Analysis of four long non-coding RNAs for hepatocellular carcinoma screening and prognosis by the aid of machine learning techniques

Samir,

Abdeldaim,

Mohammed

et al. 2024

Sci Rep

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Hepatocellular carcinoma (HCC) represents a significant health burden in Egypt, largely attributable to the endemic prevalence of hepatitis B and C viruses. Early identification of HCC remains a challenge due to the lack of widespread screening among at-risk populations. The objective of this study was to assess the utility of machine learning in predicting HCC by analyzing the combined expression of lncRNAs and conventional laboratory biomarkers. Plasma levels of four lncRNAs (LINC00152, LINC00853, UCA1, and GAS5) were quantified in a cohort of 52 HCC patients and 30 age-matched controls. The individual diagnostic performance of each lncRNA was assessed using ROC curve analysis. Subsequently, a machine learning model was constructed using Python’s Scikit-learn platform to integrate these lncRNAs with additional clinical laboratory parameters for HCC diagnosis. Individual lncRNAs exhibited moderate diagnostic accuracy, with sensitivity and specificity ranging from 60 to 83% and 53–67%, respectively. In contrast, the machine learning model demonstrated superior performance, achieving 100% sensitivity and 97% specificity. Notably, a higher LINC00152 to GAS5 expression ratio significantly correlated with increased mortality risk. The integration of lncRNA biomarkers with conventional laboratory data within a machine learning framework demonstrates significant potential for developing a precise and cost-effective diagnostic tool for HCC. To enhance the model’s robustness and prognostic capabilities, future studies should incorporate larger cohorts and explore a wider array of lncRNAs.

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Hepatocellular Carcinoma Detection Using Machine Learning Techniques

Cited by 5 publications

References 8 publications

Systematic Review of Supervised Machine Learning Models in Prediction of Medical Conditions

Systematic Review of Supervised Machine Learning Models in Prediction of Medical Conditions

Current Status and Analysis of Machine Learning in Hepatocellular Carcinoma

Analysis of four long non-coding RNAs for hepatocellular carcinoma screening and prognosis by the aid of machine learning techniques

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