A multimodal deep learning infused with artificial algae algorithm – An architecture of advanced E-health system for cancer prognosis prediction

Journal of King Saud University: Computer and Information Sciences

Summrina Kanwal¹  Sultan Alamri¹  Faiza Khan² 

[1] Department of Computing and Informatics, Saudi Electronic University, Riyadh 11673, Saudi Arabia;Riphah International University, Faculty of Computing, Islamabad 45211, Pakistan;

关键词: Cancer prognosis prediction;    Multimodal data;    Artificial Algae Algorithm (AAA);    DDQN (Double DEEP Q-NETWORK);    CNN-XGBOOST (Convolution eXtreme Gradient Boosting);    CNN-SVM (Convolution Support Vector Machine);   

DOI  :  

来源: DOAJ

【 摘 要 】

Cancer becomes life-threatening when it affects vital organs and their potential to function; therefore, early discovery is crucial for prognosis management. Medical research benefits from the rapid growth of Artificial Intelligence (AI) technology, notably machine learning (ML), deep learning (DL), and reinforcement learning (RL) which has resulted in the development of several approaches and techniques for improving cancer prediction and survival analysis. To improve cancer prognosis prediction and survival analysis, a novel framework that fuses multimodal features via early and late fusion techniques using DL and RL is presented. The approach utilizes an artificial algae algorithm (AAA) to extract the most resilient features from the dataset and build a robust model for cancer prognosis and prediction. The uniqueness of this technique is illustrated by using AAA to extract valuable features from a variety of data modalities infused with Double DEEP Q-NETWORK (DDQN), Convolution eXtreme Gradient Boosting (CNN-XGBOOST), and Convolution Support Vector Machine (CNN-SVM) models for cancer prognostic prediction. The effectiveness of the proposed framework was evaluated using publicly-available datasets: Lower Grade Glioma in the Brain(BRAIN-TCGA), Prostate Cancer, Bladder Cancer, Metastatic Colorectal Cancer (MSKCC), and the METABRIC dataset. The optimum accuracy achieved was 0.99 for the BRAIN-TCGA dataset, surpassing a variety of state-of-the-art techniques. The performance assessment results reveal that the proposed model outperforms currently available cancer prognostic prediction models in terms of predictive performance.

【 授权许可】

Unknown