Around 7% of the global population has congenital hemoglobin disorders, with over 300,000 new cases of α-thalassemia annually. Diagnosis is costly and inaccurate in low-income regions, often relying on complete blood count (CBC) tests. This study employs machine learning (ML) to classify α-thalassemia traits based on gender and CBC, exploring the effects of grouping silent- and non-carriers. The dataset includes 288 individuals with suspected α-thalassemia from Sri Lanka. It was classified using eleven discriminant formulae and nine ML models. Outliers were removed using Mahalanobis distance, and resampling was conducted with the synthetic minority oversampling technique (SMOTE) and SMOTE-nominal continuous (NC). The Mann-Whitney U test handled feature extraction and class grouping. ML performance was evaluated with eight criteria. The Ehsani formula achieved an area under the receiver operating characteristic curve (ROC-AUC) of 0.66 by grouping silent- and non-carriers. The convolutional neural network (CNN) without feature extraction demonstrated better performance, with an accuracy of 0.85, sensitivity of 0.8, specificity of 0.86, and ROC-AUC of 0.95/0.93 (micro/macro). Performance was maintained even without preprocessing. ML models outperformed classical discriminant formulae in classifying α-thalassemia using sex and CBC features. A larger dataset could enhance ML model generalization and the impact of feature extraction. Grouping silent- and non-carriers improved ML results, especially with resampling. The silent carriers were not separable from non-carriers regarding the available features.

Background: Haemoglobin S (HbS) and C (HbC) are the most important sickling variants on the African continent, imposing major health burdens. Early detection of carrier status is crucial but often hindered by resource limitations. Objectives: To develop machine learning (ML) models to accurately classify HbS and HbC carriers using readily available routine blood tests, facilitating cost‐effective mass screening. Methods: We utilised demographic and routine blood parameters from 469,248 individuals from the UK general population, including 1635 individuals with HbS and/or HbC variants identified by whole exome sequencing, to develop ML models for carrier detection based on standard blood tests. Three ML models (Logistic Regression [LR], Random Forest [RF] and XGBoost [XGB]) were trained using 32 different standard blood test results. Results: All models demonstrated high discriminatory ability (ROC‐AUC: LR 0.951; RF 0.943; XGB 0.956) in the UK general population. At a sensitivity of 95%, specificities were 77% (LR), 76% (RF) and 78% (XGB). SHAP analysis revealed consistent key features across models. When use was restricted to black individuals, performance fell considerably. Conclusions: ML models based on routine blood tests effectively identify HbS and HbC carriers in a mixed general population. This approach has the potential to enhance screening efficiency by reducing reliance on specialised techniques.

The design of satellite constellations is a complex optimization problem interdependent with other decision problems and multiple competing, user-specific criteria. Consequently, it is very difficult to make a final decision on the constellation design. This study proposes a full simulation and evaluation framework for designing a satellite constellation. Firstly, constructing a solution space by constraining orbital parameters and varying satellite count and plane configuration. Secondly, employing six evaluation metrics—covering both cost and coverage—that are weighted via the case company, Sternula’s setting, with the TOPSIS approach for ranking the candidate constellations. A subsequent sensitivity analysis evaluates robustness to shifts in criterion weights and per-satellite cost. The study indicates that a Walker Star constellation with 97.5° inclination, 105 satellites in 15 planes (phasing 7) achieves the best cost–coverage balance for the case company and remains stable under weight and cost variations.

Purpose: A scoping review of the published risk stratification scores for febrile neutropenia (FN) was performed to provide a basis for further research and optimization of risk stratification models that can support evidence-based clinical decision-making with a combined individual patient and health resource perspective. Methods: The scoping review utilized the Preferred Reporting Items for Systematic Reviews and Meta-analysis extension for Scoping Reviews (PRISMA-ScR). Studies reporting risk stratification models for FN and published in the PubMed and/or Scopus databases between 2000 and 2024 were retrieved and reviewed. Study eligibility criteria were adult cancer patients and articles utilizing FN risk stratification methods. Two researchers reviewed all relevant studies separately to determine if they were eligible for inclusion and extracted the necessary data. Results: A total of 210 papers was screened by title and A further 158 were screened by retrieval and eligibility, and 14 studies were found eligible after reviewing full papers. Studies have different cohort sizes (min 31, max 4434), age and gender distributions, cancer types (1 hematological, 3 gynecological, 10 mixtures of hematological and solid cancers), definitions of FN and complication, study type (2 retrospective, 12 prospective). The resulting papers mostly focused on validating CISNE and MASCC scores. Additionally, they investigated possible improvements by evaluating revised versions of the MASCC score. Conclusions: The scoping review revealed inconsistencies in key definitions when using risk stratification scores. It is concluded that the field could benefit from more consensus in definitions and research approaches to secure the generalizability and utility of the research.