Scopus Citation Performance

Dear Readers,

We are delighted to share the annual report on the citation performance of Journal Medical Informatics Technology (MEDINFTech). In recent years, MEDINFTech has consistently maintained a reputation for being a leading institution in the field of Biomedical Informatics. The annual citation metrics compiled by Scopus show that the scholarly community has recognized our commitment to publishing exceptional and ground-breaking research.

This report highlights the achievement of MEDINFTech in receiving a significant amount of citations in the Scopus database since 2023. The annual citation count serves as evidence of the research published in MEDINFTech being both relevant and impactful, while also highlighting the journal's increasing importance in the academic world.

As we contemplate this accomplishment, we express our sincere appreciation to our outstanding authors, devoted reviewers, and industrious editorial team for their steadfast support and dedication to achieving excellence. Through the combined efforts of its members, MEDINFTech maintains its success as a platform for sharing advanced research and promoting academic discussions.

In the future, we are committed to maintaining the highest standards of academic publishing and to increasing the visibility and influence of MEDINFTech worldwide. Year after year, we continue to demonstrate our commitment to progressing knowledge and promoting innovation in the areas of Biomedical Informatics.

[1] S. Nuarini, “Optimization of Breast Cancer Prediction using Optimaze Parameter on Machine Learning,” Journal Medical Informatics Technology, pp. 25–30, Mar. 2023, doi: 10.37034/medinftech.v1i1.5.

[a] Mamta, S. V. Patil, M. Alate, and A. Pagrotra, “Hierarchical Deep Reinforcement Learning with Neural Turing Machines for Treatment Path Optimization,” 2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), pp. 1–6, Dec. 2023, doi: 10.1109/icaiihi57871.2023.10489355.

[2]  W. Bismi and J. Na`am, “Classification of Myopia Levels using Deep Learning Methods on Fundus Image,” Journal Medical Informatics Technology, pp. 42–48, Jun. 2023, doi: 10.37034/medinftech.v1i2.8.

[a] N. Rizzieri, L. Dall’Asta, and M. Ozoliņš, “An Automated Diagnosis of Myopia from an Optic Disc Image Using YOLOv11: A Feasible Approach for Non-Expert ECPs in Computer Vision,” Life, vol. 15, no. 10, p. 1495, Sep. 2025, doi: 10.3390/life15101495.

[b] N. Rizzieri, L. Dall’Asta, and M. Ozoliņš, “Myopia Detection from Eye Fundus Images: New Screening Method Based on You Only Look Once Version 8,” Applied Sciences, vol. 14, no. 24, p. 11926, Dec. 2024, doi: 10.3390/app142411926.

[3] S. Hadianti, “Optimization of The Machine Learning Approach using Optuna in Heart Disease Prediction,” Journal Medical Informatics Technology, pp. 59–64, Sep. 2023, doi: 10.37034/medinftech.v1i3.15.

[a] D. A. Abdel Hady, O. M. Mabrouk, and T. Abd El-Hafeez, “Employing machine learning for enhanced abdominal fat prediction in cavitation post-treatment,” Scientific Reports, vol. 14, no. 1, May 2024, doi: 10.1038/s41598-024-60387-x.

[b] H. Herianto, “Machine Learning Algorithm Optimization using Stacking Technique for Graduation Prediction,” Journal of Applied Data Sciences, vol. 5, no. 3, pp. 1272–1285, Sep. 2024, doi: 10.47738/jads.v5i3.316.

[c] J. J. Gabriel and L. Jani Anbarasi, “Accurate Cardiovascular Disease Prediction: Leveraging Opt_hpLGBM With Dual-Tier Feature Selection,” IEEE Access, vol. 12, pp. 142427–142448, 2024, doi: 10.1109/access.2024.3470537.

[d] L.-H. Lai et al., “The Use of Machine Learning Models with Optuna in Disease Prediction,” Electronics, vol. 13, no. 23, p. 4775, Dec. 2024, doi: 10.3390/electronics13234775.

[e] P. Sridevi, Z. Arefin, and S. I. Ahamed, “An integrated machine learning and hyperparameter optimization framework for noninvasive creatinine estimation using photoplethysmography signals,” Healthcare Analytics, vol. 7, p. 100395, Jun. 2025, doi: 10.1016/j.health.2025.100395.

[f] N. A. Rahmi, S. Defit, and -. Okfalisa, “The Use of Hyperparameter Tuning in Model Classification: A Scientific Work Area Identification,” JOIV : International Journal on Informatics Visualization, vol. 8, no. 4, p. 2181, Dec. 2024, doi: 10.62527/joiv.8.4.3092.

[g] Ginanti Riski, Dedy Hartama, and Solikhun, “Optimizing Multilayer Perceptron for Car Purchase Prediction with GridSearch and Optuna,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 9, no. 2, pp. 266–275, Mar. 2025, doi: 10.29207/resti.v9i2.6328.

[h] P. Chairmadurai, P. Kavitha, and S. Kamalakkannan, “Enhanced Bayesian Optimized Support Vector Machine (BO-SVM) Classification and Prediction of Heart Disease,” 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL), pp. 1044–1051, Feb. 2025, doi: 10.1109/icsadl65848.2025.10933457.

[i] A. Efendi, I. Fitri, and G. W. Nurcahyo, “Improvement of Machine Learning Algorithms with Hyperparameter Tuning on Various Datasets,” 2024 International Conference on Future Technologies for Smart Society (ICFTSS), pp. 75–79, Aug. 2024, doi: 10.1109/icftss61109.2024.10691354.

[4] H. Dwi Saputra, A. I. E. Efendi, E. Rudini, D. Riana, and A. S. Hewiz, “Hepatitis Prediction Using K-NN, Naive Bayes, Support Vector Machine, Multilayer Perceptron and Random Forest, Gradient Boosting, K-Means,” Journal Medical Informatics Technology, pp. 96–100, Dec. 2023, doi: 10.37034/medinftech.v1i4.21.

[a] N. T. Deotale, S. D. Chauhan, B. Nadar, S. Nalge, and G. Thangaraj, “A non-invasive ensemble learning approach using stacking and voting models for classifying Hepatitis A, B, C, D, and E,” Discover Applied Sciences, Apr. 2026, doi: 10.1007/s42452-025-07762-z.