Medical text classification (MTC) poses significant challenges in health informatics due to contextual complexity, class imbalance, and limited labeled data. This study introduces HeaRT (Health Representation Transformer), a novel and explainable classification framework tailored for sparsely labeled and semantically complex medical texts. To the best of our knowledge, this is the first study to systematically combine TF-based lexical weighting, SBERT contextual embeddings, and cluster-enhanced structural features within a unified multi-vector fusion architecture for MTC. HeaRT combines TF-based lexical weighting with SBERT embeddings to capture both lexical and contextual nuances. A hybrid feature selection mechanism based on ANOVA and SHAP is employed to reduce redundancy and improve interpretability. To further enhance representational capacity, cluster-derived features from K-means, DBSCAN, and Agglomerative Clustering are added, introducing topological structure awareness into the learning process. The proposed framework is evaluated on two benchmark datasets: Medical Abstracts and the Biomedical Text Dataset, and is compared against state-of-the-art models such as BERT, and Doc2Sequence. Experimental results reveal that HeaRT achieves an F1-score of 60.74% with AdaBoost on Medical Abstracts and 94.02% with LightGBM on the Biomedical Text Dataset. Paired t-test results confirm the statistical significance of these gains (p < 0.05). These findings establish HeaRT as a robust, interpretable, and extensible solution for medical text classification, with strong potential for deployment in clinical decision support, biomedical literature mining, and healthcare informatics applications.

Postural control becomes fragile by aging which increases the risk of falls, leads to fractures, and decreases the quality of life. It is important to investigate the fall risks in people with the older adults. In our study, we analyzed the KINECAL dataset which contains the records of 90 participants aged 18 to 92 years performing clinical movements. We performed six feature selection methods (the Information Gain, Fisher Score (GFS), Recursive Feature Elimination (RFE), Principal Component Analysis (PCA), Chi-square (CS), and Correlation-Based Feature Selection (CBFS)) and four classification algorithms (Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and Decision Tree (DT)). Our findings showed that XGBoost consistently outperformed the other algorithms, achieving the highest accuracy and F1 scores across most feature selection methods. XGBoost achieved its highest F1 scores with the original dataset (98.28%), CBFS (98.03%), and Information Gain (97.85%). On the other hand, Correlation-Based Feature Selection and Fisher Score generally achieved higher results among the feature selection methods. Conversely, the Support Vector Machine algorithm generally performed poorly. These findings strongly suggest that machine learning methods, particularly ensemble techniques like XGBoost, can be effectively utilized to predict fall risk, and they offer a foundation for developing new approaches in rehabilitation protocols. The study highlights the critical importance of selecting appropriate feature selection methods to improve the performance and interpretability of models used in rehabilitation.

Yazılım projelerinde, çalışma takvimine göre projenin belirlenen sürede yetişmeme durumu ortaya çıkabilir. Böyle durumlarda fazla mesai yapılması çözüm olarak görülmektedir. Fakat çalışma saatlerinin artmasının proje sonucuna etkisi belirsizdir. Bu çalışmada fazla mesainin, çalışanlar ve proje üzerindeki etkilerinin incelenmesi için çalışanların fazla mesai ile çalışma saatlerinin arttırılması konusundaki düşüncelerini almak amacıyla, çevrimiçi katılım sağlanarak cevaplanacak şekilde anket hazırlanmıştır. 2019 Kasım ayı boyunca, çevrimiçi ortamda aktif olan ve 34 sorudan oluşan ankete, yazılım projelerinde görev alan 180 kişi katılmıştır. Çalışmanın sonucunda, fazla mesaiden dolayı çalışanların dinlenemedikleri, fiziksel sağlık sorunlarının ortaya çıktığı ve sosyal yaşamlarının olumsuz etkilendiği ortaya çıkmıştır. Ayrıca art arda yapılan fazla mesailerin, çalışanların mesai saatleri içerisindeki üretkenliğini de olumsuz etkilediği belirlenmiştir.