Loading...

A hierarchical machine learning model based on Glioblastoma patients' clinical, biomedical, and image data to analyze their treatment plans

Ershadi, M. M ; Sharif University of Technology | 2022

343 Viewed
  1. Type of Document: Article
  2. DOI: 10.1016/j.compbiomed.2022.106159
  3. Publisher: Elsevier Ltd , 2022
  4. Abstract:
  5. Aim of study: Glioblastoma Multiforme (GBM) is an aggressive brain cancer in adults that kills most patients in the first year due to ineffective treatment. Different clinical, biomedical, and image data features are needed to analyze GBM, increasing complexities. Besides, they lead to weak performances for machine learning models due to ignoring physicians' knowledge. Therefore, this paper proposes a hierarchical model based on Fuzzy C-mean (FCM) clustering, Wrapper feature selection, and twelve classifiers to analyze treatment plans. Methodology/Approach: The proposed method finds the effectiveness of previous and current treatment plans, hierarchically determining the best decision for future treatment plans for GBM patients using clinical data, biomedical data, and different image data. A case study is presented based on the Cancer Genome Atlas Glioblastoma Multiforme dataset to prove the effectiveness of the proposed model. This dataset is analyzed using data preprocessing, experts' knowledge, and a feature reduction method based on the Principal Component Analysis. Then, the FCM clustering method is utilized to reinforce classifier learning. Outcomes of study: The proposed model finds the best combination of Wrapper feature selection and classifier for each cluster based on different measures, including accuracy, sensitivity, specificity, precision, F-score, and G-mean according to a hierarchical structure. It has the best performance among other reinforced classifiers. Besides, this model is compatible with real-world medical processes for GBM patients based on clinical, biomedical, and image data. © 2022 Elsevier Ltd
  6. Keywords:
  7. Classification & clustering ; Glioblastoma ; Image processing ; Cluster analysis ; Diseases ; Feature selection ; Hierarchical systems ; Image analysis ; Learning systems ; Medical imaging ; Patient treatment ; Principal component analysis ; Reduction ; Biomedical data ; Classification/clustering ; Clinical data ; Feature reduction & feature selection ; Features reductions ; Features selection ; Glioblastoma multiforme ; Glioblastomas ; Hierarchical model ; Images processing ; Classification (of information) ; Bayesian learning ; Bleeding ; Brain cortex ; Cancer patient ; Case based reasoning ; Classifier ; Clinical assessment ; Clinical effectiveness ; Clinical feature ; Clinical study ; Compression ; Cyst ; Data processing ; Decision tree ; Discriminant analysis ; Feature extraction ; Fuzzy c means clustering ; Fuzzy system ; Fuzzy type i inference system ; Genome analysis ; Hopfield neural network ; Human ; k means clustering ; k nearest neighbor ; Learning vector quantization ; Logistic regression analysis ; Machine learning ; Methodology ; Multilayer perceptron ; Nuclear magnetic resonance imaging ; Professional knowledge ; Radial basis function neural network ; Random forest ; Signal noise ratio ; Support vector machine ; Treatment planning ; Tumor localization ; Tumor volume ; Wrapper feature selection ; X-ray computed tomography
  8. Source: Computers in Biology and Medicine ; Volume 150 , 2022 ; 00104825 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0010482522008678?via%3Dihub