Explainable machine learning approach for cancer prediction through binarilization of RNA sequencing data

In recent years, researchers have proven the effectiveness and speediness of machine learning-based cancer diagnosis models. However, it is difficult to explain the results generated by machine learning models, especially ones that utilized complex high-dimensional data like RNA sequencing data. In...

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Bibliographic Details
Published in:PloS one 2024-05, Vol.19 (5), p.e0302947-e0302947
Main Authors: Chen, Tianjie, Kabir, Md Faisal
Format: Article
Language:English
Subjects:
Cancer
Correlation coefficient
Correlation coefficients
Data collection
Data processing
Datasets
Decision Trees
Feature selection
Gene sequencing
Genetic algorithms
Genetic aspects
Humans
Learning algorithms
Machine Learning
Medical research
Neoplasms - genetics
Neural networks
Neural Networks, Computer
Oncology, Experimental
Peptides
Performance evaluation
Prediction models
Researchers
Ribonucleic acid
RNA
RNA sequencing
ROC Curve
Sequence Analysis, RNA - methods
Support Vector Machine
Support vector machines
Survival analysis
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Summary:In recent years, researchers have proven the effectiveness and speediness of machine learning-based cancer diagnosis models. However, it is difficult to explain the results generated by machine learning models, especially ones that utilized complex high-dimensional data like RNA sequencing data. In this study, we propose the binarilization technique as a novel way to treat RNA sequencing data and used it to construct explainable cancer prediction models. We tested our proposed data processing technique on five different models, namely neural network, random forest, xgboost, support vector machine, and decision tree, using four cancer datasets collected from the National Cancer Institute Genomic Data Commons. Since our datasets are imbalanced, we evaluated the performance of all models using metrics designed for imbalance performance like geometric mean, Matthews correlation coefficient, F-Measure, and area under the receiver operating characteristic curve. Our approach showed comparative performance while relying on less features. Additionally, we demonstrated that data binarilization offers higher explainability by revealing how each feature affects the prediction. These results demonstrate the potential of data binarilization technique in improving the performance and explainability of RNA sequencing based cancer prediction models.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0302947