SavedAdd your journal

Journals / Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi / 2026 / Cilt: 16 Sayı: 2

Comparison of Different Machine Learning Models with Data Balancing for Prediction of Cardiovascular Disease Risks Based on Big Data

Büyük Verilere Dayalı Kardiyovasküler Hastalık Risklerinin Tahmini İçin Veri Dengeleme ile Farklı Makine Öğrenmesi Modellerinin Karşılaştırılması

Gözde Özsezer(Çanakkale Onsekiz Mart University, Faculty of Health Sciences, Department of Nursing)0000-0003-4352-1124
Pages
461–487
Publication Date
2026-06-01
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Results demonstrate that data balancing, especially SMOTE-ENN, enhances model sensitivity, aiding CVD risk identification.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Cardiovascular diseases (CVD) are a leading global cause of death and morbidity.
📊
Key Findings & Data:The 2021 CDC BRFSS dataset, with 308,854 records, was preprocessed by removing missing and irrelevant data.
💡
Conclusion & Impact:Results demonstrate that data balancing, especially SMOTE-ENN, enhances model sensitivity, aiding CVD risk identification.
🔬Design:Modeling & Simulation
📍Scope:2021 Verileri
⚙️Variables:kardiyovasküler hastalıklar · makine öğrenimi · büyük veri

Abstract

Cardiovascular diseases (CVD) are a leading global cause of death and morbidity. This study evaluates data balancing techniques (SMOTE, ENN, SMOTE-ENN, SMOTE-Tomek) and machine learning (ML) algorithms for predicting CVD risk using big data. The 2021 CDC BRFSS dataset, with 308,854 records, was preprocessed by removing missing and irrelevant data. The dataset was split into 80% training and 20% testing subsets. ML models, including logistic regression, random forest, LightGBM, XGBoost, and CatBoost, were trained on balanced data. Performance metrics such as accuracy, precision, recall, F1 score, ROC curve, and AUC were used for evaluation. SMOTE-ENN and SMOTE-Tomek improved model performance, with LightGBM and CatBoost achieving the highest AUC and F1 scores. Results demonstrate that data balancing, especially SMOTE-ENN, enhances model sensitivity, aiding CVD risk identification. These findings underscore the potential for ML in nursing to develop targeted interventions and improve outcomes.

Özet

Kardiyovasküler hastalıklar (KVH), önde gelen küresel ölüm ve morbidite nedenidir. Bu çalışma, büyük veri kullanarak KVH riskini tahmin etmek için veri dengeleme tekniklerini (SMOTE, ENN, SMOTE-ENN, SMOTE-Tomek) ve makine öğrenimi (ML) algoritmalarını değerlendirir. 308.854 kayıt içeren 2021 CDC BRFSS veri kümesi, eksik ve alakasız veriler kaldırılarak ön işleme tabi tutuldu. Veri kümesi, %80 eğitim ve %20 test alt kümelerine ayrıldı. Lojistik regresyon, rastgele orman, LightGBM, XGBoost ve CatBoost dahil olmak üzere ML modelleri, dengeli veriler üzerinde eğitildi. Doğruluk, kesinlik, geri çağırma, F1 puanı, ROC eğrisi ve AUC gibi performans ölçütleri değerlendirme için kullanıldı. SMOTE-ENN ve SMOTE-Tomek model performansını iyileştirdi, LightGBM ve CatBoost en yüksek AUC ve F1 puanlarına ulaştı. Sonuçlar, özellikle SMOTE-ENN olmak üzere veri dengelemenin model duyarlılığını artırarak kardiyovasküler hastalık riskinin belirlenmesine yardımcı olduğunu göstermektedir. Bu bulgular, halk sağlığı hemşireliğinde makine öğreniminin hedefli müdahaleler geliştirme ve sonuçları iyileştirme potansiyelinin altını çizmektedir.

🔖

Cite This Article

Özsezer, G. (2026). Comparison of Different Machine Learning Models with Data Balancing for Prediction of Cardiovascular Disease Risks Based on Big Data. Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Cilt: 16 Sayı: 2, 461–487. https://doi.org/10.21597/jist.1800624

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (80)

  1. Ali, Z. A., Abduljabbar, Z. H., Taher, H. A., Sallow, A. B., & Almufti, S. M. (2023). Exploring the power of eXtreme gradient boosting algorithm in machine learning: A review. Academic Journal of Nawroz University, 12(2), 320-334.
  2. Aslan, E., & Özüpak, Y. (2025a). Improving Accuracy Through Preprocessing and Data Augmentation Techniques with a Deep Learning-Based Approach for Arrhythmia Detection. International Journal of Integrated Engineering, 17(5), 376-388.
  3. Aslan, E., & Özüpak, Y. (2025b). Comparison of machine learning algorithms for automatic prediction of Alzheimer disease. Journal of the Chinese Medical Association, 88(2), 98-107.
  4. Aslan, E., Özüpak, Y., & Alpsalaz, F. (2025a). Boiler efficiency and performance optimization in district heating and cooling systems with machine learning models. Journal of the Chinese Institute of Engineers, 1-16.
  5. Aslan, E., Ozupak, Y., Alpsalaz, F., & Elbarbary, Z. M. (2025b). A Hybrid Machine Learning Approach for Predicting Power Transformer Failures Using Internet of Things Based Monitoring and Explainable Artificial Intelligence. IEEE Access.
  6. Assyifa, D. S., & Luthfiarta, A. (2024). SMOTE-Tomek Re-sampling Based on Random Forest Method to Overcome Unbalanced Data for Multi-class Classification. Inform: Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi, 9(2), 151-160.
  7. Baddah, W., Qasem, H. A., Alsabry, A., Al Gawani, R. S., Alzuraiqi, W. M., & Hanash, F. E. (2024, August). Optimizing Heart Disease Prediction Models through SMOTE: Addressing Data Imbalance. In 2024 4th International Conference on Emerging Smart Technologies and Applications (eSmarTA) (pp. 1-10). IEEE.
  8. Barker, J., Li, X., Khavandi, S., Koeckerling, D., Mavilakandy, A., Pepper, C.,. & Ng, G. A. (2022). Machine learning in sudden cardiac death risk prediction: a systematic review. Europace, 24(11), 1777-1787.
  9. Bentéjac, C., Csörgő, A., & Martínez-Muñoz, G. (2021). A comparative analysis of gradient boosting algorithms. Artificial Intelligence Review, 54, 1937-1967.
  10. Boateng, E. Y., Otoo, J., & Abaye, D. A. (2020). Basic tenets of classification algorithms K-nearest-neighbor, support vector machine, random forest and neural network: A review. Journal of Data Analysis and Information Processing, 8(4), 341-357.
  11. Borup, D., Christensen, B. J., Mühlbach, N. S., & Nielsen, M. S. (2023). Targeting predictors in random forest regression. International Journal of Forecasting, 39(2), 841-868.
  12. Bounab, R., Guelib, B., & Zarour, K. (2024). A Novel Machine Learning Approach For Handling Imbalanced Data: Leveraging SMOTE-ENN and XGBoost. In 2024 6th International Conference on Pattern Analysis and Intelligent Systems (PAIS) (pp. 1-7). IEEE.
  13. Charalambous, A., & Dodlek, N. (2023, June). Big data, machine learning, and artificial intelligence to advance cancer care: opportunities and challenges. In Seminars in oncology nursing (Vol. 39, No. 3, p. 151429). WB Saunders.
  14. Cheng, C. H., Lee, B. J., Nfor, O. N., Hsiao, C. H., Huang, Y. C., & Liaw, Y. P. (2024). Using machine learning-based algorithms to construct cardiovascular risk prediction models for Taiwanese adults based on traditional and novel risk factors. BMC Medical Informatics and Decision Making, 24(1), 199.
  15. Chowdhury, M. M., Ayon, R. S., & Hossain, M. S. (2024). An investigation of machine learning algorithms and data augmentation techniques for diabetes diagnosis using class imbalanced BRFSS dataset. Healthcare Analytics, 5, 100297.
  16. Cuevas-Chávez, A., Sánchez-Jiménez, E., Hernández, Y., & Ortiz-Hernandez, J. (2023, September). Class Balancing in a Cardiovascular Disease Domain. In 2023 Mexican International Conference on Computer Science (ENC) (pp. 1-8). IEEE.
  17. Cummings, G. G., Lee, S., Tate, K., Penconek, T., Micaroni, S. P., Paananen, T., & Chatterjee, G. E. (2021). The essentials of nursing leadership: A systematic review of factors and educational interventions influencing nursing leadership. International journal of nursing studies, 115, 103842.
  18. Das, A. (2024). Logistic regression. In Encyclopedia of Quality of Life and Well-Being Research (pp. 3985-3986). Cham: Springer International Publishing.
  19. Dhanalakshmi, S., Das, S., & Senthil, R. (2024). Speech features-based Parkinson’s disease classification using combined SMOTE-ENN and binary machine learning. Health and Technology, 14(2), 393-406.
  20. Dritsas, E., & Trigka, M. (2023). Efficient data-driven machine learning models for cardiovascular diseases risk prediction. Sensors, 23(3), 1161.
  21. Elreedy, D., & Atiya, A. F. (2019). A comprehensive analysis of synthetic minority oversampling technique (SMOTE) for handling class imbalance. Information Sciences, 505, 32-64.
  22. Geng, Y., Li, Q., Yang, G., & Qiu, W. (2024). Logistic regression. In Practical Machine Learning Illustrated with KNIME (pp. 99-132). Singapore: Springer Nature Singapore.
  23. Guo, Z., Wang, X., & Ge, L. (2023). Classification prediction model of indoor PM2. 5 concentration using CatBoost algorithm. Frontiers in Built Environment, 9, 1207193.
  24. Hairani, H., Anggrawan, A., & Priyanto, D. (2023). Improvement performance of the random forest method on unbalanced diabetes data classification using Smote-Tomek Link. JOIV: international journal on informatics visualization, 7(1), 258-264.
  25. Hao, L., & Huang, G. (2023). An improved AdaBoost algorithm for identification of lung cancer based on electronic nose. Heliyon, 9(3).
  26. Hussain, L., Lone, K. J., Awan, I. A., Abbasi, A. A., & Pirzada, J. U. R. (2022). Detecting congestive heart failure by extracting multimodal features with synthetic minority oversampling technique (SMOTE) for imbalanced data using robust machine learning techniques. Waves in Random and Complex Media, 32(3), 1079-1102.
  27. Igwama, G. T., Olaboye, J. A., Maha, C. C., Ajegbile, M. D., & Abdul, S. (2024). Big data analytics for epidemic forecasting: Policy Frameworks and technical approaches. International Journal of Applied Research in Social Sciences, 6(7), 1449-1460.
  28. Jamthikar, A., Gupta, D., Saba, L., Khanna, N. N., Araki, T., Viskovic, K.,. & Suri, J. S. (2020). Cardiovascular/stroke risk predictive calculators: a comparison between statistical and machine learning models. Cardiovascular diagnosis and therapy, 10(4), 919.
  29. Javaid, M., Haleem, A., Singh, R. P., Suman, R., & Rab, S. (2022). Significance of machine learning in healthcare: Features, pillars and applications. International Journal of Intelligent Networks, 3, 58-73.
  30. Jayatilake, S. M. D. A. C., & Ganegoda, G. U. (2021). Involvement of machine learning tools in healthcare decision making. Journal of healthcare engineering, 2021(1), 6679512.
  31. Khang, A., Abdullayev, V., Ali, R. N., Bali, S. Y., Mammadaga, G. M., & Hafiz, M. K. (2024). Using big data to solve problems in the field of medicine. In Computer Vision and AI-Integrated IoT Technologies in the Medical Ecosystem (pp. 407-418). CRC Press.
  32. Krittanawong, C., Virk, H. U. H., Bangalore, S., Wang, Z., Johnson, K. W., Pinotti, R.,. & Tang, W. W. (2020). Machine learning prediction in cardiovascular diseases: a meta-analysis. Scientific reports, 10(1), 16057.
  33. Kumari, J., Kumar, E., & Kumar, D. (2023). A structured analysis to study the role of machine learning and deep learning in the healthcare sector with big data analytics. Archives of Computational Methods in Engineering, 30(6), 3673-3701.
  34. Li, C., Liu, X., Shen, P., Sun, Y., Zhou, T., Chen, W.,. & Gao, P. (2024). Improving cardiovascular risk prediction through machine learning modelling of irregularly repeated electronic health records. European Heart Journal-Digital Health, 5(1), 30-40.
  35. Liu, T., Krentz, A., Lu, L., & Curcin, V. (2024). Machine learning based prediction models for cardiovascular disease risk using electronic health records data: systematic review and meta-analysis. European Heart Journal-Digital Health, ztae080.
  36. Liu, W., Laranjo, L., Klimis, H., Chiang, J., Yue, J., Marschner, S.,. & Chow, C. K. (2023). Machine-learning versus traditional approaches for atherosclerotic cardiovascular risk prognostication in primary prevention cohorts: a systematic review and meta-analysis. European Heart Journal-Quality of Care and Clinical Outcomes, 9(4), 310-322.
  37. Louridi, N., Douzi, S., & El Ouahidi, B. (2023, January). Detection of Common Risk Factors Leading to the Cardiovascular Illness Using Machine Learning. In The International Conference on Intelligent System and Smart Technologies (pp. 85-94). Cham: Springer International Publishing.
  38. Luengo-Fernandez, R., Walli-Attaei, M., Gray, A., Torbica, A., Maggioni, A. P., Huculeci, R.,. & Leal, J. (2023). Economic burden of cardiovascular diseases in the European Union: a population-based cost study. European heart journal, 44(45), 4752-4767.
  39. Mathur, P., Srivastava, S., Xu, X., & Mehta, J. L. (2020). Artificial intelligence, machine learning, and cardiovascular disease. Clinical Medicine Insights: Cardiology, 14, 1179546820927404.
  40. Misra, P. K., Kumar, N., Misra, A., & Khang, A. (2023). Heart disease prediction using logistic regression and random forest classifier. In Data-Centric AI Solutions and Emerging Technologies in the Healthcare Ecosystem (pp. 83-112). CRC Press.
  41. Mohan, S., Thirumalai, C., & Srivastava, G. (2019). Effective heart disease prediction using hybrid machine learning techniques. IEEE access, 7, 81542-81554.
  42. Narayanana, J. (2024). Implementation of Efficient Machine Learning Techniques for Prediction of Cardiac Disease using SMOTE. Procedia Computer Science, 233, 558-569.
  43. Nizam‐Ozogur, H., & Orman, Z. (2024). A heuristic‐based hybrid sampling method using a combination of SMOTE and ENN for imbalanced health data. Expert Systems, e13596.
  44. Noorunnahar, M., Chowdhury, A. H., & Mila, F. A. (2023). A tree based eXtreme Gradient Boosting (XGBoost) machine learning model to forecast the annual rice production in Bangladesh. PloS one, 18(3), e0283452.
  45. Ogundipe, D. O. (2024). The impact of big data on healthcare product development: A theoretical and analytical review. International Medical Science Research Journal, 4(3), 341-360.
  46. Omotehinwa, T. O., Oyewola, D. O., & Moung, E. G. (2024). Optimizing the light gradient-boosting machine algorithm for an efficient early detection of coronary heart disease. Informatics and Health, 1(2), 70-81.
  47. Özsezer, G., & Mermer, G. (2024). Prediction of drinking water quality with machine learning models: A public health nursing approach. Public Health Nursing, 41(1), 175-191.
  48. Özüpak, Y., Alpsalaz, F., & Aslan, E. (2025). Air Quality Forecasting Using Machine Learning: Comparative Analysis and Ensemble Strategies for Enhanced Prediction. Water, Air, & Soil Pollution, 236(7), 464.
  49. Pal, M., Parija, S., Panda, G., Dhama, K., & Mohapatra, R. K. (2022). Risk prediction of cardiovascular disease using machine learning classifiers. Open Medicine, 17(1), 1100-1113.
  50. Papachristou, N., Kotronoulas, G., Dikaios, N., Allison, S. J., Eleftherochorinou, H., Rai, T.,. & Bamidis, P. D. (2023, June). Digital transformation of cancer care in the era of big data, artificial intelligence and data-driven interventions: navigating the field. In Seminars in oncology nursing (Vol. 39, No. 3, p. 151433). WB Saunders.
  51. Patel, R. K., Aggarwal, E., Solanki, K., Dahiya, O., & Yadav, S. A. (2023). A Logistic Regression and Decision Tree Based Hybrid Approach to Predict Alzheimer's Disease. In 2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES) (pp. 722-726). IEEE.
  52. Putra, L. G. R., Marzuki, K., & Hairani, H. (2023). Correlation-based feature selection and Smote-Tomek Link to improve the performance of machine learning methods on cancer disease prediction. Engineering & Applied Science Research, 50(6).
  53. Qiu, X. (2024). Nurse-led intervention in the management of patients with cardiovascular diseases: a brief literature review. BMC nursing, 23(1), 6.
  54. Rahim, A., Rasheed, Y., Azam, F., Anwar, M. W., Rahim, M. A., & Muzaffar, A. W. (2021). An integrated machine learning framework for effective prediction of cardiovascular diseases. IEEE Access, 9, 106575-106588.
  55. Rai, H. M., Chatterjee, K., & Dashkevych, S. (2022). The prediction of cardiac abnormality and enhancement in minority class accuracy from imbalanced ECG signals using modified deep neural network models. Computers in Biology and Medicine, 150, 106142.
  56. Ramadhan, N. G., Maharani, W., & Gozali, A. A. (2024, August). Prediction of Cardiovascular Disease (CVD) in the Upcoming Year Using Tree-Based Ensemble Model. In 2024 12th International Conference on Information and Communication Technology (ICoICT) (pp. 210-216). IEEE.
  57. Rathod, Y., Komare, A., Ajgaonkar, R., Chindarkar, S., Nagare, G., Punjabi, N., & Karpate, Y. (2022). Predictive analysis of polycystic ovarian syndrome using CatBoost algorithm. In 2022 IEEE region 10 symposium (TENSYMP) (pp. 1-6). IEEE.
  58. Sai, M. J., Chettri, P., Panigrahi, R., Garg, A., Bhoi, A. K., & Barsocchi, P. (2023). An ensemble of Light Gradient Boosting Machine and adaptive boosting for prediction of type-2 diabetes. International Journal of Computational Intelligence Systems, 16(1), 14.
  59. Said, B., Bouguenna, F. I., Ayyoub, Z., Abdullah, M., Özüpak, Y., Bouddou, R.,. & Zaitsev, I. (2025). Hybrid MPC–Third‐Order Sliding Mode Control With MRAS for Fault‐Tolerant Speed Regulation of PMSMs Under Sensor Failures. International Transactions on Electrical Energy Systems, 2025(1), 5984024.
  60. Shan, W., Li, D., Liu, S., Song, M., Xiao, S., & Zhang, H. (2024). A random feature mapping method based on the AdaBoost algorithm and results fusion for enhancing classification performance. Expert Systems with Applications, 256, 124902.
  61. Shi, M., Hu, W., Li, M., Zhang, J., Song, X., & Sun, W. (2023). Ensemble regression based on polynomial regression-based decision tree and its application in the in-situ data of tunnel boring machine. Mechanical Systems and Signal Processing, 188, 110022.
  62. Suyal, M., & Goyal, P. (2022). A review on analysis of K-nearest neighbor classification machine learning algorithms based on supervised learning. International Journal of Engineering Trends and Technology, 70(7), 43-48.
  63. Tan, S. M., Han, E., Quek, R. Y. C., Singh, S. R., Gea‐Sánchez, M., & Legido‐Quigley, H. (2020). A systematic review of community nursing interventions focusing on improving outcomes for individuals exhibiting risk factors of cardiovascular disease. Journal of advanced nursing, 76(1), 47-61.
  64. Tompra, K. V., Papageorgiou, G., & Tjortjis, C. (2024). Strategic Machine Learning Optimization for Cardiovascular Disease Prediction and High-Risk Patient Identification. Algorithms, 17(5), 178.
  65. Türkiye İstatistik Kurumu. (2024). Ölüm ve Ölüm Nedeni İstatistikleri, 2023. https://data.tuik.gov.tr/Bulten/Index?p=Olum-ve-Olum-Nedeni-Istatistikleri-2023-53709
  66. Wade, C., & Glynn, K. (2020). Hands-On Gradient Boosting with XGBoost and scikit-learn: Perform accessible machine learning and extreme gradient boosting with Python. Packt Publishing Ltd.
  67. Wang, A. X., Chukova, S. S., & Nguyen, B. P. (2023). Synthetic minority oversampling using edited displacement-based k-nearest neighbors. Applied Soft Computing, 148, 110895.
  68. Wang, K., Tian, J., Zheng, C., Yang, H., Ren, J., Li, C.,. & Zhang, Y. (2021). Improving risk identification of adverse outcomes in chronic heart failure using SMOTE+ ENN and machine learning. Risk management and healthcare policy, 2453-2463.
  69. World Health Organization (2021). Cardiovascular diseases (CVDs). https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)
  70. Zhou, Y., Wang, S., Xie, Y., Zeng, J., & Fernandez, C. (2024). Remaining useful life prediction and state of health diagnosis of lithium-ion batteries with multiscale health features based on optimized CatBoost algorithm. Energy, 300, 131575.
  71. Barker, J., Li, X., Khavandi, S., Koeckerling, D., Mavilakandy, A., Pepper, C.,. & Ng, G. A. (2022). Machine learning in sudden cardiac death risk prediction: a systematic review. Europace, 24(11), 1777-1787.
  72. Jamthikar, A., Gupta, D., Saba, L., Khanna, N. N., Araki, T., Viskovic, K.,. & Suri, J. S. (2020). Cardiovascular/stroke risk predictive calculators: a comparison between statistical and machine learning models. Cardiovascular diagnosis and therapy, 10(4), 919.
  73. Krittanawong, C., Virk, H. U. H., Bangalore, S., Wang, Z., Johnson, K. W., Pinotti, R.,. & Tang, W. W. (2020). Machine learning prediction in cardiovascular diseases: a meta-analysis. Scientific reports, 10(1), 16057.
  74. Li, C., Liu, X., Shen, P., Sun, Y., Zhou, T., Chen, W.,. & Gao, P. (2024). Improving cardiovascular risk prediction through machine learning modelling of irregularly repeated electronic health records. European Heart Journal-Digital Health, 5(1), 30-40.
  75. Liu, W., Laranjo, L., Klimis, H., Chiang, J., Yue, J., Marschner, S.,. & Chow, C. K. (2023). Machine-learning versus traditional approaches for atherosclerotic cardiovascular risk prognostication in primary prevention cohorts: a systematic review and meta-analysis. European Heart Journal-Quality of Care and Clinical Outcomes, 9(4), 310-322.
  76. Luengo-Fernandez, R., Walli-Attaei, M., Gray, A., Torbica, A., Maggioni, A. P., Huculeci, R.,. & Leal, J. (2023). Economic burden of cardiovascular diseases in the European Union: a population-based cost study. European heart journal, 44(45), 4752-4767.
  77. Papachristou, N., Kotronoulas, G., Dikaios, N., Allison, S. J., Eleftherochorinou, H., Rai, T.,. & Bamidis, P. D. (2023, June). Digital transformation of cancer care in the era of big data, artificial intelligence and data-driven interventions: navigating the field. In Seminars in oncology nursing (Vol. 39, No. 3, p. 151433). WB Saunders.
  78. Said, B., Bouguenna, F. I., Ayyoub, Z., Abdullah, M., Özüpak, Y., Bouddou, R.,. & Zaitsev, I. (2025). Hybrid MPC–Third‐Order Sliding Mode Control With MRAS for Fault‐Tolerant Speed Regulation of PMSMs Under Sensor Failures. International Transactions on Electrical Energy Systems, 2025(1), 5984024.
  79. Wang, K., Tian, J., Zheng, C., Yang, H., Ren, J., Li, C.,. & Zhang, Y. (2021). Improving risk identification of adverse outcomes in chronic heart failure using SMOTE+ ENN and machine learning. Risk management and healthcare policy, 2453-2463.
  80. Zhou, Y., Wang, S., Xie, Y., Zeng, J., & Fernandez, C. (2024). Remaining useful life prediction and state of health diagnosis of lithium-ion batteries with multiscale health features based on optimized CatBoost algorithm. Energy, 300, 131575.