Comparison of various approaches to combine logistic regression with genetic algorithms in survival prediction of hepatocellular carcinoma

Książek, Wojciech; Gandor, Michał; Pławiak, Paweł

doi:10.1016/j.compbiomed.2021.104431

Cited by 47 publications

(34 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method is based on replacing missing data with a values obtained from related cases from all database records 38 . The algorithm was chosen based on previous HCC mortality studies 19,30 . This algorithm was also successfully used to solve other problems, such as supplementing data in the day‐ahead PV generation forecasting 39 and predicting mortality in patients suffering from hepatitis 40 …”

Section: Methodsmentioning

confidence: 99%

“…The proper algorithm configuration can be difficult to obtain. Here, a genetic algorithm has been used to maximize the values of fitness functions according to previous studies by the authors 15,19,30 . For a detailed description of the parameters used in the study, see Table 2.…”

Section: Methodsmentioning

confidence: 99%

“…As part of this experiment, we tested 3, 5, and 7 nearest neighbors. We chose this algorithm based on our previous experience in this field 19,30 . In the next step, we performed a typical data scaling operation using the following algorithm: standard scaler (Section 2.2).…”

Section: Methodsmentioning

confidence: 99%

“…Regarding the best model, the authors achieved 75.19% classification accuracy and 66.5% F1‐score. Thanks to the public release by Santos et al, 12 their dataset was used by multiple authors 13–30 . All proposed models were predicted in terms of classification accuracy and/or F‐1 score.…”

Section: Introductionmentioning

confidence: 99%

“…Their proposed model obtained 92.12% accuracy and 91.61% F1‐score. Ksiażek et al 30 proposed a combination of genetic algorithm and logistic regression models. The authors gained 94.55% classification accuracy and 93.56% F1‐score.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

NCA‐GA‐SVM: A new two‐level feature selection method based on neighborhood component analysis and genetic algorithm in hepatocellular carcinoma fatality prognosis

Książek

Turza

Pławiak

2022

Numer Methods Biomed Eng

Self Cite

View full text Add to dashboard Cite

Hepatocellular carcinoma (HCC) is one of the major challenges facing biomedical research. Despite the high lethality, methods to predict mortality for this type of aggressive malignant tumor are insufficient. Machine learning is recognized by many authors as a valuable, yet poorly studied tool in this field. Undoubtedly, searching for new feature selection methods is significant in building an effective machine-learning model. In this study, we propose the novel hybrid model using neighborhood components analysis, genetic algorithm and support vector machine classifier (NCA-GA-SVM). Because SVM works with default parameters characterized by low classification results, we decided to use GA for the proper optimization and feature selection. As reported in the available literature, NCA and GA obtain high classification results. Here, we decided to combine these approaches, building a two-level algorithm for HCC fatality prognosis. We used a well-known dataset collected from 165 patients at Coimbra's Hospital and University Center, Portugal. Our results revealed 96.36% classification accuracy and 95.52% F1-score. Additionally, we compared all data for these metrics published so far. We demonstrated that our algorithm achieved the highest accuracy and can be successfully applied for the assessment of hepatocellular carcinoma mortality in the future. Our findings bring methodological value for future HCC studies and emphasize the possibility of using machine-learning techniques to improve the quality of medical decisions.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

NCA‐GA‐SVM: A new two‐level feature selection method based on neighborhood component analysis and genetic algorithm in hepatocellular carcinoma fatality prognosis

Książek

Turza

Pławiak

2022

Numer Methods Biomed Eng

Self Cite

View full text Add to dashboard Cite

show abstract

Intelligent diagnosis system of hepatitis C virus: A probabilistic neural network based approach

Terlapu

Gedela

Gangu

et al. 2022

Int J Imaging Syst Tech

View full text Add to dashboard Cite

In recent years, early detection of hepatitis C virus (HCV) disease has been a vital task in the medical science field. HCV became the main health concern to the public, as it was noticed to have more blood donors in Egypt equated to other nationalities. The WHO assessed that in 2019, around 290 000 individuals died from hepatitis C, which says the seriousness of the HCV disease. So, early prediction, preventions, and curing the disease are vital components to save individuals from HCV. In this paper, we propose and investigate experimental results of the five machine learning (ML) models and probabilistic neural network (PNN) based approach to detect the HCV utilizing University of Califonia Irvine (UCI) ML Egyptian HCV dataset. We also analyze the statistical reports of HCV stages and their features. As per result analysis, the random forest (RF) ML model performs superiorly to other traditional ML algorithms with 97.5% of accuracy. The PNN (incremental hidden layer [HL] neurons) based proposal model shows a very high performance (99.6% of accuracy) at 30-HL neurons of PNN. As per comparative analysis, the proposed model is superior to experimental basic ML models, and early HCV disease works are related to this area. This research focuses on early detection, prevention, and challenges of handling HCV.

show abstract

Social Service Management System Based on Logistic Regression Algorithm

2022

Cyber Security Intelligence and Analytics

View full text Add to dashboard Cite

Comparison of various approaches to combine logistic regression with genetic algorithms in survival prediction of hepatocellular carcinoma

Cited by 47 publications

References 40 publications

NCA‐GA‐SVM: A new two‐level feature selection method based on neighborhood component analysis and genetic algorithm in hepatocellular carcinoma fatality prognosis

NCA‐GA‐SVM: A new two‐level feature selection method based on neighborhood component analysis and genetic algorithm in hepatocellular carcinoma fatality prognosis

Intelligent diagnosis system of hepatitis C virus: A probabilistic neural network based approach

Social Service Management System Based on Logistic Regression Algorithm

Contact Info

Product

Resources

About