Framework for Race-Specific Prostate Cancer Detection Using Machine Learning Through Gene Expression Data: Feature Selection Optimization Approach
Abstract BackgroundPrevious machine learning approaches for prostate cancer detection using gene expression data have shown remarkable classification accuracies. However, prior studies overlook the influence of racial diversity within the population and the importance of selec...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2025-07-01
|
| Series: | JMIR Bioinformatics and Biotechnology |
| Online Access: | https://bioinform.jmir.org/2025/1/e72423 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849238984520105984 |
|---|---|
| author | David Agustriawan Adithama Mulia Marlinda Vasty Overbeek Vincent Kurniawan Jheno Syechlo Moeljono Widjaja Muhammad Imran Ahmad |
| author_facet | David Agustriawan Adithama Mulia Marlinda Vasty Overbeek Vincent Kurniawan Jheno Syechlo Moeljono Widjaja Muhammad Imran Ahmad |
| author_sort | David Agustriawan |
| collection | DOAJ |
| description |
Abstract
BackgroundPrevious machine learning approaches for prostate cancer detection using gene expression data have shown remarkable classification accuracies. However, prior studies overlook the influence of racial diversity within the population and the importance of selecting outlier genes based on expression profiles.
ObjectiveWe aim to develop a classification method for diagnosing prostate cancer using gene expression in specific populations.
MethodsThis research uses differentially expressed gene analysis, receiver operating characteristic analysis, and MSigDB (Molecular Signature Database) verification as a feature selection framework to identify genes for constructing support vector machine models.
ResultsAmong the models evaluated, the highest observed accuracy was achieved using 139 gene features without oversampling, resulting in 98% accuracy for White patients and 97% for African American patients, based on 388 training samples and 92 testing samples. Notably, another model achieved a similarly strong performance, with 97% accuracy for White patients and 95% for African American patients, using only 9 gene features. It was trained on 374 samples and tested on 138 samples.
ConclusionsThe findings identify a race-specific diagnosis method for prostate cancer detection using enhanced feature selection and machine learning. This approach emphasizes the potential for developing unbiased diagnostic tools in specific populations. |
| format | Article |
| id | doaj-art-da277ea0d7104e00a8fee349d48a08e5 |
| institution | Kabale University |
| issn | 2563-3570 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Bioinformatics and Biotechnology |
| spelling | doaj-art-da277ea0d7104e00a8fee349d48a08e52025-08-20T04:01:16ZengJMIR PublicationsJMIR Bioinformatics and Biotechnology2563-35702025-07-016e72423e7242310.2196/72423Framework for Race-Specific Prostate Cancer Detection Using Machine Learning Through Gene Expression Data: Feature Selection Optimization ApproachDavid Agustriawanhttp://orcid.org/0000-0003-1185-1145Adithama Muliahttp://orcid.org/0009-0005-6885-0575Marlinda Vasty Overbeekhttp://orcid.org/0000-0003-2590-843XVincent Kurniawanhttp://orcid.org/0009-0004-1238-5232Jheno Syechlohttp://orcid.org/0009-0001-5557-5085Moeljono Widjajahttp://orcid.org/0000-0003-3002-7426Muhammad Imran Ahmadhttp://orcid.org/0000-0002-9157-5998 Abstract BackgroundPrevious machine learning approaches for prostate cancer detection using gene expression data have shown remarkable classification accuracies. However, prior studies overlook the influence of racial diversity within the population and the importance of selecting outlier genes based on expression profiles. ObjectiveWe aim to develop a classification method for diagnosing prostate cancer using gene expression in specific populations. MethodsThis research uses differentially expressed gene analysis, receiver operating characteristic analysis, and MSigDB (Molecular Signature Database) verification as a feature selection framework to identify genes for constructing support vector machine models. ResultsAmong the models evaluated, the highest observed accuracy was achieved using 139 gene features without oversampling, resulting in 98% accuracy for White patients and 97% for African American patients, based on 388 training samples and 92 testing samples. Notably, another model achieved a similarly strong performance, with 97% accuracy for White patients and 95% for African American patients, using only 9 gene features. It was trained on 374 samples and tested on 138 samples. ConclusionsThe findings identify a race-specific diagnosis method for prostate cancer detection using enhanced feature selection and machine learning. This approach emphasizes the potential for developing unbiased diagnostic tools in specific populations.https://bioinform.jmir.org/2025/1/e72423 |
| spellingShingle | David Agustriawan Adithama Mulia Marlinda Vasty Overbeek Vincent Kurniawan Jheno Syechlo Moeljono Widjaja Muhammad Imran Ahmad Framework for Race-Specific Prostate Cancer Detection Using Machine Learning Through Gene Expression Data: Feature Selection Optimization Approach JMIR Bioinformatics and Biotechnology |
| title | Framework for Race-Specific Prostate Cancer Detection Using Machine Learning Through Gene Expression Data: Feature Selection Optimization Approach |
| title_full | Framework for Race-Specific Prostate Cancer Detection Using Machine Learning Through Gene Expression Data: Feature Selection Optimization Approach |
| title_fullStr | Framework for Race-Specific Prostate Cancer Detection Using Machine Learning Through Gene Expression Data: Feature Selection Optimization Approach |
| title_full_unstemmed | Framework for Race-Specific Prostate Cancer Detection Using Machine Learning Through Gene Expression Data: Feature Selection Optimization Approach |
| title_short | Framework for Race-Specific Prostate Cancer Detection Using Machine Learning Through Gene Expression Data: Feature Selection Optimization Approach |
| title_sort | framework for race specific prostate cancer detection using machine learning through gene expression data feature selection optimization approach |
| url | https://bioinform.jmir.org/2025/1/e72423 |
| work_keys_str_mv | AT davidagustriawan frameworkforracespecificprostatecancerdetectionusingmachinelearningthroughgeneexpressiondatafeatureselectionoptimizationapproach AT adithamamulia frameworkforracespecificprostatecancerdetectionusingmachinelearningthroughgeneexpressiondatafeatureselectionoptimizationapproach AT marlindavastyoverbeek frameworkforracespecificprostatecancerdetectionusingmachinelearningthroughgeneexpressiondatafeatureselectionoptimizationapproach AT vincentkurniawan frameworkforracespecificprostatecancerdetectionusingmachinelearningthroughgeneexpressiondatafeatureselectionoptimizationapproach AT jhenosyechlo frameworkforracespecificprostatecancerdetectionusingmachinelearningthroughgeneexpressiondatafeatureselectionoptimizationapproach AT moeljonowidjaja frameworkforracespecificprostatecancerdetectionusingmachinelearningthroughgeneexpressiondatafeatureselectionoptimizationapproach AT muhammadimranahmad frameworkforracespecificprostatecancerdetectionusingmachinelearningthroughgeneexpressiondatafeatureselectionoptimizationapproach |