A Comparative Performance Evaluation of Random Forest Feature Selection on Classification of Hepatocellular Carcinoma Gene Expression Data

Latief, Moh Abdul; Siswantining, Titin; Bustamam, Alhadi; Sarwinda, Devvi

doi:10.1109/icicos48119.2019.8982435

Cited by 10 publications

(2 citation statements)

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“…The purpose of bicluster analysis varies, one of which is early detection of a disease that attacks genetics such as Alzheimer's, cancer, and tumors. Ardaneswari, Bustamam, and Siswantining (2017) and Latief, Siswantining, Bustamam, and Sarwinda (2019) analyzed gene expression data of carcinoma tumors and hepatocellular carcinoma, respectively. Ardaneswari, Bustamam, and Siswantining (2017) leveraged a parallel k-means algorithm for two-phase method biclustering, while Latief, Siswantining, Bustamam, and Sarwinda (2019) classified the gene expression data using random forest feature selection.…”

Section: Introductionmentioning

confidence: 99%

“…Ardaneswari, Bustamam, and Siswantining (2017) and Latief, Siswantining, Bustamam, and Sarwinda (2019) analyzed gene expression data of carcinoma tumors and hepatocellular carcinoma, respectively. Ardaneswari, Bustamam, and Siswantining (2017) leveraged a parallel k-means algorithm for two-phase method biclustering, while Latief, Siswantining, Bustamam, and Sarwinda (2019) classified the gene expression data using random forest feature selection. In this research, the implementation of the plaid model method was carried out in gene expression data of colon cancer.…”

Section: Introductionmentioning

confidence: 99%

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Biclustering Analysis Using Plaid Model on Gene Expression Data of Colon Cancer

Siswantining

Aminanto

Sarwinda

et al. 2021

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Unlike other typical clustering analysis, which considers column only, biclustering analysis processes a matrix into sub-matrices based on rows and columns simultaneously. One method of bicluster analysis uses the probabilistic model, like the plaid model, that provides overlapping bicluster. The plaid model calculates the value of an element given from a particular sub-matrix for each cell; thus, the value can be seen as the number of contributions of a particular bicluster. The algorithm begins with preparing the input data as a matrix, then an initial model is assessed and makes a residual matrix from the model. After that, we determine bicluster candidates, which are evaluated for its effect parameters and bicluster membership parameters. Finally, the bicluster candidate is pruned to give the optimal bicluster. We implemented the algorithm on gene expression dataset of colon cancer, where the rows and columns contain observations and types of genes, respectively. We carried out in six distinct scenarios in which each scenario uses different model parameters and threshold values. We measured the results using Jaccard index and coherence variance. Our experiments show that biclustering analysis on a model with mean, row, and column effects of colon cancer data output low coherence variance.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Biclustering Analysis Using Plaid Model on Gene Expression Data of Colon Cancer

Siswantining

Aminanto

Sarwinda

et al. 2021

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Identifying the risk of depression in a large sample of adolescents: An artificial neural network based on random forest

Zhou,

Zhang,

Gong

et al. 2024

Journal of Adolescence

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BackgroundThis study aims to develop an artificial neural network (ANN) prediction model incorporating random forest (RF) screening ability for predicting the risk of depression in adolescents and identifies key risk factors to provide a new approach for primary care screening of depression among adolescents.MethodsThe data were from a large cross‐sectional study conducted in China from July to September 2021, enrolling 8635 adolescents aged 10–17 with their parents. We used the Patient health questionnaire (PHQ‐9) to rate adolescent depression symptoms, using scales and single‐item questions to collect demographic information and other variables. Initial model variables screening used the RF importance assessment, followed by building prediction model using the screened variables through the ANN.ResultsThe rate of depression symptoms in adolescents was 24.6%, and the depression risk prediction model was built based on 70% of the training set and 30% of the test set. Ten variables were included in the final prediction model with a model accuracy of 85.03%, AUC of 0.892, specificity of 89.79%, and sensitivity of 70.81%. The top 10 significant factors of depression risk were adolescent rumination, adolescent self‐esteem, adolescent mobile phone addiction, peer victimization, care in parenting styles, overprotection in parenting styles, academic pressure, conflict in parent–child relationship, parental rumination, and relationship between parents.ConclusionsThe ANN model based on the RF effectively identifies depression risk in adolescents and provides a methodological reference for large‐scale primary screening. Cross‐sectional studies and single‐item scales limit further improvements in model accuracy.

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Cervical Cancerous Cell Detection Using Enhanced Classification and Embedded Deep Learning Method

Shanu

Prova

2023

Communications in Computer and Information Science

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A Comparative Performance Evaluation of Random Forest Feature Selection on Classification of Hepatocellular Carcinoma Gene Expression Data

Cited by 10 publications

References 10 publications

Biclustering Analysis Using Plaid Model on Gene Expression Data of Colon Cancer

Biclustering Analysis Using Plaid Model on Gene Expression Data of Colon Cancer

Identifying the risk of depression in a large sample of adolescents: An artificial neural network based on random forest

Cervical Cancerous Cell Detection Using Enhanced Classification and Embedded Deep Learning Method

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