B. Kalpana¹, S. Dhanasekaran², T. Abirami³, Ashit Kumar Dutta⁴, Marwa Obayya⁵, Jaber S. Alzahrani⁶, Manar Ahmed Hamza^7,*

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2243-2257, 2023, DOI:10.32604/csse.2023.027129

Abstract Biomedical data classification has become a hot research topic in recent years, thanks to the latest technological advancements made in healthcare. Biomedical data is usually examined by physicians for decision making process in patient treatment. Since manual diagnosis is a tedious and time consuming task, numerous automated models, using Artificial Intelligence (AI) techniques, have been presented so far. With this motivation, the current research work presents a novel Biomedical Data Classification using Cat and Mouse Based Optimizer with AI (BDC-CMBOAI) technique. The aim of the proposed BDC-CMBOAI technique is to determine the occurrence of diseases using biomedical data. Besides, the… More >

Amal Al-Rasheed¹, Jaber S. Alzahrani², Majdy M. Eltahir³, Abdullah Mohamed⁴, Anwer Mustafa Hilal^5,*, Abdelwahed Motwakel⁵, Abu Sarwar Zamani⁵, Mohamed I. Eldesouki⁶

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3275-3290, 2022, DOI:10.32604/cmc.2022.029823

Abstract The biomedical data classification process has received significant attention in recent times due to a massive increase in the generation of healthcare data from various sources. The developments of artificial intelligence (AI) and machine learning (ML) models assist in the effectual design of medical data classification models. Therefore, this article concentrates on the development of optimal Stacked Long Short Term Memory Sequence-to-Sequence Autoencoder (OSAE-LSTM) model for biomedical data classification. The presented OSAE-LSTM model intends to classify the biomedical data for the existence of diseases. Primarily, the OSAE-LSTM model involves min-max normalization based pre-processing to scale the data into uniform format.… More >

Mahmoud Ragab^1,2,3,*, Diaa Hamed^4,5

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 4185-4200, 2022, DOI:10.32604/cmc.2022.027074

Abstract Medical data classification becomes a hot research topic in the healthcare sector to aid physicians in the healthcare sector for decision making. Besides, the advances of machine learning (ML) techniques assist to perform the effective classification task. With this motivation, this paper presents a Fuzzy Clustering Approach Based on Breadth-first Search Algorithm (FCA-BFS) with optimal support vector machine (OSVM) model, named FCABFS-OSVM for medical data classification. The proposed FCABFS-OSVM technique intends to classify the healthcare data by the use of clustering and classification models. Besides, the proposed FCABFS-OSVM technique involves the design of FCABFS technique to cluster the medical data… More >