Predicting Post-Surgical Complications using Machine Learning Models for Patients with Brain Tumors
Abstract
The focus of this study is to enhance clinical decision-making and post-operative care by investigating the application of machine learning (ML) models to predict post-surgical problems in patients with brain tumors. To improve recovery and lower morbidity, problems like infections, seizures, and cerebrospinal fluid leaks must be identified early. This study resolves the challenges of conventional prediction techniques and illustrates the future potential of AI in neurosurgery by using open-access datasets. The purpose of this study is to use de-identified, publicly accessible dataset to create a machine learning (ML) model for predicting post-surgical complications in patients with brain tumors. A retrospective cohort approach was used, and 850 adult patients who had brain tumor resection surgery and were at least 18 years old were included. We gathered pre-operative clinical and radiological data as well as post-operative complication data. Predicting binary outcomes (complications vs. no complications) was done using four machine learning models: logistic regression, random forest, XGBoost, and neural networks. Neural networks had the highest accuracy with 87.6 percent. On the other hand, logistic regression had the lowest accuracy with 80.1 percent. Findings showed that the neural network model performed better than the others, obtaining the greatest F1-score and AUROC. Clinical uses of this model could be used to forecast post-operative problems in patients with brain tumors. We assessed performance parameters such as F1-score, accuracy, precision, recall, and area under the receiver operating characteristic curve (AUROC).
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N. N. Mostafa, “Human brain tumors detection using neutrosophic c-means clustering algorithm,” J. Neutrosophic Fuzzy Syst., vol. 1, no. 1, pp. 55–58, 2021, doi: 10.54216/JNFS.010106.
M. P. Nasrallah et al., “Machine learning for cryosection pathology predicts the 2021 WHO classification of glioma,” Med (New York, N.Y.), vol. 4, no. 8, pp. 526-540.e4, Aug. 2023, doi: 10.1016/J.MEDJ.2023.06.002.
“(PDF) Ensemble of Deep Learning approaches for Detection of Brain Tumor.” Accessed: Jan. 12, 2025. [Online]. Available: https://www.researchgate.net/publication/371378003_Ensemble_of_Deep_Learning_approaches_for_Detection_of_Brain_Tumor
V. S. Lotlikar, N. Satpute, and A. Gupta, “Brain Tumor Detection Using Machine Learning and Deep Learning: A Review,” Curr. Med. imaging, vol. 18, no. 6, pp. 604–622, Sep. 2022, doi: 10.2174/1573405617666210923144739.
K. Lakshmi, S. Amaran, G. Subbulakshmi, S. Padmini, G. P. Joshi, and W. Cho, “Explainable artificial intelligence with UNet based segmentation and Bayesian machine learning for classification of brain tumors using MRI images,” Sci. Rep., vol. 15, no. 1, Dec. 2025, doi: 10.1038/S41598-024-84692-7.
J. Luo, M. Pan, K. Mo, Y. Mao, and D. Zou, “Emerging role of artificial intelligence in diagnosis, classification and clinical management of glioma,” Semin. Cancer Biol., vol. 91, pp. 110–123, Jun. 2023, doi: 10.1016/J.SEMCANCER.2023.03.006.
I. Pan and R. Y. Huang, “Artificial intelligence in neuroimaging of brain tumors: reality or still promise?,” Curr. Opin. Neurol., vol. 36, no. 6, pp. 549–556, Dec. 2023, doi: 10.1097/WCO.0000000000001213.
X. P. Liu et al., “Clinical significance and molecular annotation of cellular morphometric subtypes in lower-grade gliomas discovered by machine learning,” Neuro. Oncol., vol. 25, no. 1, pp. 68–81, Jan. 2023, doi: 10.1093/NEUONC/NOAC154.
M. Javed, M. H. Bajwa, and S. K. Bakhshi, “Artificial intelligence- image learning and its applications in neurooncology: a review,” J. Pak. Med. Assoc., vol. 74, no. 4 (Supple-4), pp. S158–S160, Apr. 2024, doi: 10.47391/JPMA.AKU-9S-24.
O. S. Al-Kadi, R. Al-Emaryeen, S. Al-Nahhas, I. Almallahi, R. Braik, and W. Mahafza, “Empowering brain cancer diagnosis: harnessing artificial intelligence for advanced imaging insights,” Rev. Neurosci., vol. 35, no. 4, pp. 399–419, Jun. 2024, doi: 10.1515/REVNEURO-2023-0115.
M. Cè et al., “Artificial Intelligence in Brain Tumor Imaging: A Step toward Personalized Medicine,” Curr. Oncol., vol. 30, no. 3, pp. 2673–2701, Mar. 2023, doi: 10.3390/CURRONCOL30030203.
M. K. H. Khan et al., “Machine learning and deep learning for brain tumor MRI image segmentation,” Exp. Biol. Med. (Maywood)., vol. 248, no. 21, pp. 1974–1992, Nov. 2023, doi: 10.1177/15353702231214259.
A. Sanchez-Aguilera et al., “Machine learning identifies experimental brain metastasis subtypes based on their influence on neural circuits,” Cancer Cell, vol. 41, no. 9, pp. 1637-1649.e11, Sep. 2023, doi: 10.1016/J.CCELL.2023.07.010.
R. Hossain, R. B. Ibrahim, and H. B. Hashim, “Automated Brain Tumor Detection Using Machine Learning: A Bibliometric Review,” World Neurosurg., vol. 175, pp. 57–68, Jul. 2023, doi: 10.1016/J.WNEU.2023.03.115.
J. Noor, A. Chaudhry, and S. Batool, “Microfluidic Technology, Artificial Intelligence, and Biosensors As Advanced Technologies in Cancer Screening: A Review Article,” Cureus, vol. 15, no. 5, May 2023, doi: 10.7759/CUREUS.39634.
N. Vobugari, V. Raja, U. Sethi, K. Gandhi, K. Raja, and S. R. Surani, “Advancements in Oncology with Artificial Intelligence-A Review Article,” Cancers (Basel)., vol. 14, no. 5, Mar. 2022, doi: 10.3390/CANCERS14051349.
Z. Qiu et al., “Novel Nano-Drug Delivery System for Brain Tumor Treatment,” Cells, vol. 11, no. 23, Dec. 2022, doi: 10.3390/CELLS11233761.
S. Reddy, K. Tatiparti, S. Sau, and A. K. Iyer, “Recent advances in nano delivery systems for blood-brain barrier (BBB) penetration and targeting of brain tumors,” Drug Discov. Today, vol. 26, no. 8, pp. 1944–1952, Aug. 2021, doi: 10.1016/J.DRUDIS.2021.04.008.
Q. D. Buchlak, N. Esmaili, J. C. Leveque, C. Bennett, F. Farrokhi, and M. Piccardi, “Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review,” J. Clin. Neurosci., vol. 89, pp. 177–198, Jul. 2021, doi: 10.1016/J.JOCN.2021.04.043.
S. Yan, S. Liu, A. Di Ieva, M. Pagnucco, and Y. Song, “Meta-transfer Learning for Brain Tumor Segmentation: Within and Beyond Glioma,” Adv. Exp. Med. Biol., vol. 1462, pp. 221–230, 2024, doi: 10.1007/978-3-031-64892-2_13.
J. Calderaro, T. P. Seraphin, T. Luedde, and T. G. Simon, “Artificial intelligence for the prevention and clinical management of hepatocellular carcinoma,” J. Hepatol., vol. 76, no. 6, pp. 1348–1361, Jun. 2022, doi: 10.1016/J.JHEP.2022.01.014.
M. A. Wahed, “Real-Time Intrusion Detection and Traffic Analysis Using AI Techniques in IoT Infrastructure,” 2024 1st Int. Conf. Emerg. Technol. Dependable Internet Things, pp. 1–6, Nov. 2024, doi: 10.1109/ICETI63946.2024.10777213.
M. A. Wahed, M. S. Alzboon, M. Alqaraleh, M. Al-Batah, A. F. Bader, and S. A. Wahed, “Enhancing Diagnostic Precision in Pediatric Urology: Machine Learning Models for Automated Grading of Vesicoureteral Reflux,” 2024 7th Int. Conf. Internet Appl. Protoc. Serv., pp. 1–7, Nov. 2024, doi: 10.1109/NETAPPS63333.2024.10823509.
M. Abdel Wahed, “Enhanced machine learning algorithm for detection and classification of phishing attacks | Abdel Wahed | International Journal of Open Information Technologies.” Accessed: Jan. 12, 2025. [Online]. Available: http://injoit.org/index.php/j1/article/view/1960
S. Mahajan, A. Dahiya, and A. Dhull, “(on-line) © IBERAMIA and the authors An Efficient Deep Learning Technique for Brain Abnormality Detection Using MRI,” Intel. Artif., vol. 28, no. 75, pp. 81–100, 2025, doi: 10.4114/intartif.vol28iss75pp81-100.
K. Lakshmi, S. Amaran, G. Subbulakshmi, S. Padmini, G. P. Joshi, and W. Cho, “Explainable artificial intelligence with UNet based segmentation and Bayesian machine learning for classification of brain tumors using MRI images,” Sci. Rep., vol. 15, no. 1, p. 690, Dec. 2025, doi: 10.1038/s41598-024-84692-7.
Msoud Nickparvar. (2021). Brain Tumor MRI Dataset [Data set]. Kaggle. https://doi.org/10.34740/KAGGLE/DSV/2645886
Wahed, Salma Abdel, and Mutaz Abdel Wahed. "Machine learning-based prediction and classification of psychiatric symptoms induced by drug and plants toxicity." Gamification and Augmented Reality 3 (2025): 3.
Wahed, Salma Abdel, and Mutaz Abdel Wahed. "AI-Driven Digital Well-being: Developing Machine Learning Model to Predict and Mitigate Internet Addiction." LatIA 3 (2025): 134-134.
Wahed, Salma Abdel Wahed Abdel, Rama Shdefat Shdefat, and Mutaz Abdel Wahed. "A Machine Learning Model for Diagnosis and Differentiation of Schizophrenia, Bipolar Disorder and Borderline Personality Disorder." LatIA 3 (2025): 133-133.
Alzboon, Mowafaq Salem, Muhyeeddin Alqaraleh, Mutaz Abdel Wahed, Abdullah Alourani, Ahmad Fuad Bader, and Mohammad Al-Batah. "AI-Driven UAV Distinction: Leveraging Advanced Machine Learning." In 2024 7th International Conference on Internet Applications, Protocols, and Services (NETAPPS), pp. 1-7. IEEE, 2024..
Alzboon, Mowafaq Salem, Muhyeeddin Alqaraleh, Mutaz Abdel Wahed, Abdullah Alourani, Ahmad Fuad Bader, and Mohammad Al-Batah. "AI-Driven UAV Distinction: Leveraging Advanced Machine Learning." In 2024 7th International Conference on Internet Applications, Protocols, and Services (NETAPPS), pp. 1-7. IEEE, 2024..
Wahed, Mutaz Abdel, Muhyeeddin Alqaraleh, Mowafaq Salem Alzboon, and Mohammad Subhi Al-Batah. "Evaluating AI and Machine Learning Models in Breast Cancer Detection: A Review of Convolutional Neural Networks (CNN) and Global Research Trends." LatIA 3 (2025): 117-117.
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