Developing A Hybrid Machine Learning Algorithm for Anemia Diagnosis

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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 the advancement of hybrid machine learning models that combine qualitative and quantitative datasets to address issues associated with blood diseases. To tackle this gap, we evaluate algorithmic combinations using datasets that include key characteristics from complete blood count (CBC) examinations. This manuscript presents an evaluation of prominent deep learning models, such as CNN, RNN, and RCNN, as part of our methodology. The assessment identified XGBoost as the optimal machine learning algorithm, and RCNN as the best deep learning model. Consequently, we propose a hybrid model named ‘RCNNX,’ which integrates Robust Scaler, SelectKBest feature selection, RCNN, and the XGBoost algorithm. The hybrid model, ‘RCNNX,’ achieves exceptional testing accuracy levels of 100% and 95.12% on the Anemia Diagnosis Dataset and a second dataset, respectively. Additionally, it demonstrates recall rates of 100% and 94.64% for the same datasets. These findings highlight the superiority of the proposed model, as it effectively utilizes feature selection to reduce the number of input variables, minimizing the risk of overfitting. Moreover, XGBoost enhances the predictive accuracy of RCNN.