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

• Published in: Computers in biology and medicine Vol. 150; p. 106159
• Main Authors: Ershadi, Mohammad Mahdi, Rise, Zeinab Rahimi, Niaki, Seyed Taghi Akhavan
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.11.2022; Elsevier Limited
• Subjects: Accuracy; Adult; Algorithms; Biomedical data; Brain; Brain cancer; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - therapy; Brain research; Cancer; Classification & clustering; Classifiers; Clustering; Data analysis; Datasets; Deep Learning; Discriminant analysis; Effectiveness; Feature reduction & feature selection; Feature selection; Genomes; Glioblastoma; Glioblastoma - diagnostic imaging; Glioblastoma - therapy; Health services; Hierarchical model; Human error; Humans; Image processing; Internal Medicine; Learning algorithms; Literature reviews; Machine Learning; Magnetic resonance imaging; Medical imaging; Neural networks; Other; Patients; Physicians; Principal components analysis; Support vector machines; Tumors; Hierarchical model; Feature reduction & feature selection; Image processing; Classification & clustering; Glioblastoma
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2022.106159

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. 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. 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. [Display omitted] •A new hierarchical model is proposed in this paper based on 12 reinforced classifiers by fuzzy C-mean (FCM).•Data preprocessing and the Principal Component Analysis feature reduction are utilized to reduce the computational time.•A real dataset based on clinical, biomedical, and image data of GBM patients is studied.•Extract proper features based on DX, CT, and MRI images along with other available meaningful data using expert's knowledge.•Determining the decision for the future treatment plan of GBM patients is the main target of the proposed model.