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%.

This paper presents the logical relationships of Aristotle’s square of opposition on four basic categorial prepositions (i.e., contrary, contradictory, subcontrary, and subaltern) of Joint Opposite Selection (JOS). JOS brings a mutual reinforcement by joining two opposition strategies of Dynamic Opposite (DO) and Selective Leading Opposition (SLO). The DO and SLO improve the balance of exploration and exploitation, respectively, in a given search space. We also propose an enhancement of Golden Jackal Optimization (GJO) with Joint Opposite Selection named GJO-JOS. In the optimization process, JOS assists GJO in assaulting the prey swiftly using SLO. DO assists GJO in finding better chances to locate the fittest prey. With JOS, the GJO succeeds in elevating its performance. We evaluate the performance of GJO-JOS in a competition of CEC 2017 on a set of 29 benchmark functions. The benchmark functions include unimodal, multimodal, hybrid, and composition. Based on these benchmark functions, the evaluation results of GJO-JOS are competitive to GJO with seven single opposition-based learning strategies (OBLs). We also compare GJO-JOS to eight nature-inspired algorithms including the original version of GJO. GJO-JOS produces promising results among seven single OBLs, eight natured-inspired algorithms, and GJO. The experimental results confirm that GJO-JOS generates equilibrium in the balance mechanism effectively.

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.

This paper proposes an unsupervised discrimination analysis for feature selection based on a property of the Fourier transform of the probability density distribution. Each feature is evaluated on the basis of a simple observation motivated by the concept of optical diffraction, which is invariant under feature scaling. The time complexity is O(mn), where m is number of features and n is number of instances when being applied directly to the given data. This approach is also extended to deal with data orientation, which is the direction of data alignment. Therefore, the discrimination score of any transformed space can be used for evaluating the original features. The experimental results on several real-world datasets demonstrate the effectiveness of the proposed method.