aspg-28-3516-1792153278 20261016122118 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 2025 9 1 Safa S. Abdul-Jabbar Computer Science Department/ College of Science for Women University of Baghdad, Baghdad, Iraq Alaa k. Farhan Computer Science Department, University of Technology, Baghdad, Iraq Abdul Hafeez Kandhro Institute of Medical Technology, Jinnah Sindh Medical University, Karachi, Pakistan The utilization of artificial intelligence (AI) algorithms has significantly transformed the field of blood disease diagnosis, enabling enhanced capabilities in prediction, categorization, and optimization. However, there is still a lack of research exploring hybrid machine learning models that combine qualitative and quantitative datasets to address issues associated with blood diseases. We evaluate algorithmic combinations using datasets that include key characteristics from complete blood count (CBC) examinations. Prominent deep learning models, including CNN, RNN, and RCNN, are assessed. XGBoost is identified as the strongest machine learning algorithm and RCNN as the strongest deep learning model. We therefore propose RCNNX, a hybrid model integrating Robust Scaler, SelectKBest feature selection, RCNN, and XGBoost. RCNNX achieves testing accuracies of 100% and 95.12% on the Anemia Diagnosis Dataset and a second dataset, respectively, with recall rates of 100% and 94.64%. These findings indicate that feature selection reduces the number of input variables and the risk of overfitting, while XGBoost improves the predictive accuracy of RCNN. 2025 15 22 3516 https://creativecommons.org/licenses/by/4.0/ 10.54216/JAIM.090103 https://www.americaspg.com/journal/28/article/3516 https://www.americaspg.com/storage/articles/manuscripts/01M4Y3JCC03PYRTDATCT108E8M.pdf