The procedure to diagnose anemia is time-consuming and resource-intensive due to the existence of a multitude of symptoms that can be felt physically or seen visually. Anemia also has several forms, which can be distinguished based on several characteristics. It is possible to diagnose anemia through a quick, affordable, and easily accessible laboratory test known as the complete blood count (CBC), but the method cannot directly identify different kinds of anemia. Therefore, further tests are required to establish a gold standard for the type of anemia in a patient. These tests are uncommon in settings that offer healthcare on a smaller scale because they require expensive equipment. Moreover, it is also difficult to discern between beta thalassemia trait (BTT), iron deficiency anemia (IDA), hemoglobin E (HbE), and combination anemias despite the presence of multiple red blood cell (RBC) formulas and indices with differing optimal cutoff values. This is due to the existence of several varieties of anemia in individuals, making it difficult to distinguish between BTT, IDA, HbE, and combinations. Therefore, a more precise and automated prediction model is proposed to distinguish these four types to accelerate the identification process for doctors. Historical data were retrieved from the Laboratory of the Department of Clinical Pathology and Laboratory Medicine, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia for this purpose. Furthermore, the model was developed using the algorithm for the extreme learning machine (ELM). This was followed by the measurement of the performance using the confusion matrix and 190 data representing the four classes, and the results showed 99.21% accuracy, 98.44% sensitivity, 99.30% precision, and an F1 score of 98.84%.

Abnormal erythrocytes have diverse shapes. The appearance of specific erythrocyte shapes in a person’s blood can indicate certain diseases, including thalassemia. We used thalassemia peripheral blood smear images and applied a segmentation process to produce single erythrocyte sub-images. Each erythrocyte has a unique shape. The selection of appropriate features to represent erythrocytes is critical for classification accuracy. We used morphological features such as moment invariants, geometry parameters of the cell and central pallor, and distance angle signature (DAS) morphological features of the cell and central pallor. We combined morphological features with texture and color features to increase the accuracy of erythrocyte classification. In this study, the multi-layer perceptron is used to classify nine shapes of erythrocytes present in thalassemia cases. The experimental results of 7108 erythrocytes indicated an accuracy of 98.11% based on the combination of features. The experimental results also show that the combination of features we proposed produced higher classification accuracy than previous work, which yielded an accuracy of 93.77%.

Anemia is a widespread worldwide health problem that has a substantial effect on groups who are particularly susceptible. The objective of this work is to improve the diagnosis of anemia by creating a hybrid machine learning model called SMOTE-MRS. This model combines SMOTE, K-Means Clustering, and Random Over Sampling techniques. The model aims to enhance diagnosis accuracy and ultimately improve healthcare outcomes, specifically in Indonesia, by resolving the imbalance in the dataset. The SMOTE-MRS model mitigates the issue of imbalanced datasets by combining the techniques of SMOTE, K-Means clustering, and random oversampling. K-Means clustering first divides the dataset into K groups. SMOTE then produces artificial instances of the underrepresented class inside every cluster. Random oversampling is a technique that replicates instances of the minority class to make the dataset balanced and enhance the training of machine learning models. The research evaluated the performance of Random Forest, Naive Bayes, and Support Vector Machine models in predicting anemia, using the SMOTE-MRS technique. The SMOTE-MRS algorithm exhibited outstanding performance, achieving scores of 0.973 for Accuracy, 0.990 for Recall, 0.968 for Precision, 0.979 for F1-Score, and 0.994 for AUC. These findings demonstrate its exceptional capacity to effectively handle class imbalance and enhance prediction accuracy. The SMOTE-MRS algorithm has outstanding performance on a wide range of datasets, such as those related to anemia, diabetes, breast cancer, and kidney failure datasets. This showcases its robustness and versatility in many predictive modeling scenarios. The findings demonstrate that SMOTE-MRS outperforms standard approaches such as SMOTE, SMOTE-ENC, and ROS in effectively addressing class imbalance. The research affirms the better performance of SMOTE-MRS in addressing class imbalance for the prediction of anemia. SMOTE-MRS surpasses traditional approaches, attaining exceptional results in all measures. Future studies should prioritize the optimization of SMOTE-MRS to minimize small performance declines, conduct a comparative analysis with other approaches, and validate its adaptability across various domains. Furthermore, it is advisable to include SMOTE-MRS in deep learning models and real-time applications.

The Broad Learning System Forest (BLSF) model proved to be the preeminent classifier across all assessed datasets, demonstrating outstanding performance and efficiency. In the dataset, BLSF attained an accuracy of 94.53%, markedly exceeding standard BLS at 84.32%, Fuzzy BLS at 79.88%, and Intuitionistic Fuzzy BLS, which obtained just 15.55%. BLSF's efficacy is exemplified by its recall of 0.9452, accuracy of 0.9524, and F1-Score of 0.9481, emphasizing its ability to accurately categorize both positive and negative samples. Despite Tree BLS demonstrating a superior accuracy of 97.19%, its substantial memory use (789,862 MB) constrains its usability. In the Breast cancer dataset, BLSF attained a notable accuracy of 98.24%, trailing only Tree BLS at 98.75%, and markedly surpassing BLS (94.95%) and Fuzzy BLS (78.68%). The AUC-ROC score of BLSF at 0.9817 further substantiates its exceptional categorization capability. Although Incremental Weighted Ensemble BLS achieved a commendable performance of 98.57%, BLSF exhibited enhanced memory efficiency and a somewhat improved F1-Score of 0.9827. The Cardiovascular disease dataset revealed that BLSF achieved an accuracy of 76.61%, surpassing BLS at 71.38% and Fuzzy BLS at 53.23%. Although Tree BLS attained a superior accuracy of 91.75%, its considerable memory demands made BLSF the more efficient option. Incremental Weighted Ensemble BLS exhibited enhanced accuracy (81.99%) but required greater memory utilization. In imbalanced datasets such as PIDD (73.60%), Kidney failure (100%), and Heart disease (91.12%), BLSF consistently surpassed Fuzzy BLS and Intuitionistic Fuzzy BLS. It got a flawless score on the Kidney failure dataset, demonstrating its proficiency in managing difficult classifications with exceptional accuracy. BLSF excelled in accuracy, precision, recall, and efficiency across many datasets. In conclusion, the Broad Learning System Forest (BLSF) is a pioneering model that excels in accuracy and efficiency while exhibiting exceptional adaptability to complex and imbalanced datasets, marking a significant advancement in medical data classification.

Anemia is a prevalent global health issue that is characterized by a deficit in red blood cells or low levels of hemoglobin. This condition is influenced by various causes, including nutritional inadequacies, chronic diseases, and genetic predisposition. The incidence of the phenomenon exhibits variation across different geographical regions and demographic groups. This pioneering research investigates the identification and classification of anemia, potentially leading to transformative advancements in the discipline. The classification of anemia encompasses four distinct groups, namely Beta Thalassemia Trait, Iron Deficiency Anemia, Hemoglobin E, and Combination. This comprehensive categorization offers clinicians a more refined and detailed comprehension of the condition. The integration of deep learning and machine learning in the Multilayer Extremely Randomized Tree Learning Machine (MERTLM) model represents a departure from traditional approaches and a significant advancement in the field of medical categorization accuracy. The MERTLM approach integrates randomized tree with multilayer extreme learning machine (M-ELM) representation learning, hence emphasizing the possibility of interdisciplinary collaboration in the field of diagnostics. In addition to its impact on anemia, artificial intelligence (AI) is playing a significant role in revolutionizing medical diagnosis by emphasizing the integration of innovative methods. This study utilizes the combined capabilities of machine learning and deep learning to improve accuracy. Notably, recent developments have resulted in an exceptional accuracy rate of 99.67%, precision of 99.60%, sensitivity of 99.47%, and an amazing F1-Score of 99.53%. This study represents a significant advancement in the field of anemia research, providing valuable insights that may be applied to intricate medical issues and enhancing the quality of patient care.