A hybrid cancer prediction based on multi-omics data and reinforcement learning state action reward state action (SARSA). (March 2023)

• Record Type: Journal Article
• Authors: Mohammed, Mazin Abed; Lakhan, Abdullah; Abdulkareem, Karrar Hameed; Garcia-Zapirain, Begoña
• Is Part Of: Computers in biology and medicine. Volume 154(2023)
• Publication Date: 2023-03
• Subjects: Multi-omics -- Workload learning -- Hybrid -- Processing -- SARSA; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2023.106617
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: These days, the ratio of cancer diseases among patients has been growing day by day. Recently, many cancer cases have been reported in different clinical hospitals. Many machine learning algorithms have been suggested in the literature to predict cancer diseases with the same class types based on trained and test data. However, there are many research rooms available for further research. In this paper, the studies look into the different types of cancer by analyzing, classifying, and processing the multi-omics dataset in a fog cloud network. Based on SARSA on-policy and multi-omics workload learning, made possible by reinforcement learning, the study made new hybrid cancer detection schemes. It consists of different layers, such as clinical data collection via laboratories and tool processes (biopsy, colonoscopy, and mammography) at the distributed omics-based clinics in the network. The study considers the different cancer classes such as carcinomas, sarcomas, leukemias, and lymphomas with their types in work and processes them using the multi-omics distributed clinics in work. In order to solve the problem, the study presents omics cancer workload reinforcement learning state action reward state action "SARSA" (OCWLS) schemes, which are made up of an on-policy learning scheme on different parameters like states, actions, timestamps, reward, accuracy, and processing time constraints. The goal is to process multiple cancer classes and workload feature matching while reducing the time it takes to process in clinical hospitals that are spread out. Simulation results show that OCWLS is better than other machine learning methods regarding+ processing time, extracting features from multiple classes of cancer, and matching in the system. Highlights: The study designs novel reinforcement SARSA on-Policy and multi-omics workload learning. The study presents OCWLS schemes, which consist of an on-policy learning scheme on different parameters. The study presents the simulation environment for the multiple cancer classes analysis and prediction.