2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 14 , Issue 1 , PP: 263-272, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Luz M. Aguirre Paz 1 * , Jorge Viteri Moya 2 , Rita Azucena D. Vásquez 3 , Darvin M. Ramírez Guerra 4 , Dekhkonov Burkhon 5
Doi: https://doi.org/10.54216/FPA.140119
Survival analysis remains an important area in predictive modeling, especially in cases where event timing information is critical. This work presents a research effort to investigate the application of LightGBM, a high-performance high-throughput model, to conduct an improved fusion of decisions from multiple trees to reach survival analysis. Our objective is to address the challenge of developing correct predictive models while advancing computational effectiveness. Based on a case study of live disaster scenarios, the proposed approach applies and compares LightGBM with traditional prediction methods, which involve careful design engineering, and model training with LightGBM tree structure refinement. The results obtained from fair experimentation and comprehensive predictive performance evaluation demonstrate the robustness of LightGBM in increasing the accuracy of relevant classification tasks toward survival analysis. Furthermore, the findings highlighted that the combination of excellent tree depth for cutting and multi-thread optimization promotes efficient computational complexity and prediction accuracy.
Survival analysis , Information Fusion , Predictive modeling , Machine learning , Prognostic analysis , Time-to-event analysis , Novel prognostic models.
[1] Gu, Yuan, Mingyue Wang, Yishu Gong, Song Jiang, Chen Li, and Dan Zhang. 2023. “Unveiling Breast Cancer Risk Profiles: A Comprehensive Survival Clustering Analysis Empowered by an Interactive Online Tool for Personalized Medicine.” MedRxiv, 2005–23.
[2] Biswas, Deeptarup, Gaurish Loya, and Graham Roy Ball. 2022. “Role of AI and ML in Empowering and Solving Problems Linked to COVID-19 Pandemic.” In Multi-Pronged Omics Technologies to Understand COVID-19, 203–18. CRC Press.
[3] Agliardi, Giulia, Anna Rita Liuzzi, Alastair Hotblack, Donatella De Feo, Nicolás Núñez, Cassandra L Stowe, Ekaterina Friebel, et al. 2021. “Intratumoral IL-12 Delivery Empowers CAR-T Cell Immunotherapy in a Pre-Clinical Model of Glioblastoma.” Nature Communications 12 (1): 444.
[4] Liu, Xiaoxue, Qirui Zhang, Jianrui Li, Qiang Xu, Zhizheng Zhuo, Junjie Li, Xian Zhou, et al. 2023. “Coordinatized Lesion Location Analysis Empowering ROI-Based Radiomics Diagnosis on Brain Gliomas.” European Radiology, 1–12.
[5] Renugadevi, M, K Narasimhan, C V Ravikumar, Rajesh Anbazhagan, Giovanni Pau, Kannan Ramkumar, Mohamed Abbas, N Raju, K Satish, and S Prabu. 2023. “Machine Learning Empowered Brain Tumor Segmentation and Grading Model for Lifetime Prediction.” IEEE Access.
[6] Abdel-Basset, M., Moustafa, N., Hawash, H. and Tari, Z., 2023. Responsible graph neural networks. CRC Press.
[7] Ivanova, Elena, Alexey Fayzullin, Victor Grinin, Dmitry Ermilov, Alexander Arutyunyan, Peter Timashev, and Anatoly Shekhter. 2023. “Empowering Renal Cancer Management with AI and Digital Pathology: Pathology, Diagnostics and Prognosis.” Biomedicines 11 (11): 2875.
[8] Matsuo, Koji, Sanjay Purushotham, Bo Jiang, Rachel S Mandelbaum, Tsuyoshi Takiuchi, Yan Liu, and Lynda D Roman. 2019. “Survival Outcome Prediction in Cervical Cancer: Cox Models vs Deep-Learning Model.” American Journal of Obstetrics and Gynecology 220 (4): 381--e1.
[9] Whitesell, Luke, Sandro Santagata, Marc L Mendillo, Nancy U Lin, David A Proia, and Susan Lindquist. 2014. “HSP90 Empowers Evolution of Resistance to Hormonal Therapy in Human Breast Cancer Models.” Proceedings of the National Academy of Sciences 111 (51): 18297–302.
[10] Muthuswamy, M. and M. Ali, A. (2023) “Sustainable Supply Chain Management in the Age of Machine Intelligence: Addressing Challenges, Capitalizing on Opportunities, and Shaping the Future Landscape”, Sustainable Machine Intelligence Journal, 3. doi: 10.61185/SMIJ.2023.33103.
[11] Adorno, Maddalena, Michelangelo Cordenonsi, Marco Montagner, Sirio Dupont, Christine Wong, Byron Hann, Aldo Solari, et al. 2009. “A Mutant-P53/Smad Complex Opposes P63 to Empower TGF$β$-Induced Metastasis.” Cell 137 (1): 87–98.
[12] Bordukova, Maria, Nikita Makarov, Raul Rodriguez-Esteban, Fabian Schmich, and Michael P Menden. 2023. “Generative Artificial Intelligence Empowers Digital Twins in Drug Discovery and Clinical Trials.” Expert Opinion on Drug Discovery, 1–10.
[13] Silva, Daswin De, Weranja Ranasinghe, Tharindu Bandaragoda, Achini Adikari, Nishan Mills, Lahiru Iddamalgoda, Damminda Alahakoon, et al. 2018. “Machine Learning to Support Social Media Empowered Patients in Cancer Care and Cancer Treatment Decisions.” PloS One 13 (10): e0205855.
[14] Cudkowicz, Merit E, Leonard H van den Berg, Jeremy M Shefner, Hiroshi Mitsumoto, Jesus S Mora, Albert Ludolph, Orla Hardiman, et al. 2013. “Dexpramipexole versus Placebo for Patients with Amyotrophic Lateral Sclerosis (EMPOWER): A Randomised, Double-Blind, Phase 3 Trial.” The Lancet Neurology 12 (11): 1059–67.
[15] D’Innocenzo, Lauren, Margaret M Luciano, John E Mathieu, M Travis Maynard, and Gilad Chen. 2016. “Empowered to Perform: A Multilevel Investigation of the Influence of Empowerment on Performance in Hospital Units.” Academy of Management Journal 59 (4): 1290–1307.
[16] Zhang, Tao, Yu Wang, Qing Li, Liangyu Lin, Chunliang Xu, Yueqing Xue, Mingyuan Hu, Yufang Shi, and Ying Wang. 2022. “Mesenchymal Stromal Cells Equipped by IFN$α$ Empower T Cells with Potent Anti-Tumor Immunity.” Oncogene 41 (13): 1866–81.
[17] Sharma, A K. 2018. “Artificial Intelligence and Advanced Analytics Technology Oriented Solutions for Empowering Indian Farmers: A Scientific Perspective.” Emerging Post-Harvest Engineering and Technological Interventions for Enhancing Farmer’s Income.
[18] Sahu, Rupsa Rani, Anjana Raut, and Swati Samantaray. 2022. “Technological Empowerment: Applications of Machine Learning in Oral Healthcare.” In 2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC), 1–4.
[19] Chen, Kun-Huang, Kung-Jeng Wang, Min-Lung Tsai, Kung-Min Wang, Angelia Melani Adrian, Wei-Chung Cheng, Tzu-Sen Yang, Nai-Chia Teng, Kuo-Pin Tan, and Ku-Shang Chang. 2014. “Gene Selection for Cancer Identification: A Decision Tree Model Empowered by Particle Swarm Optimization Algorithm.” BMC Bioinformatics 15 (1): 1–10.
[20] MacKenzie, Todd, Alex H Gifford, Kathryn A Sabadosa, Hebe B Quinton, Emily A Knapp, Christopher H Goss, and Bruce C Marshall. 2014. “Longevity of Patients with Cystic Fibrosis in 2000 to 2010 and beyond: Survival Analysis of the Cystic Fibrosis Foundation Patient Registry.” Annals of Internal Medicine 161 (4): 233–41.
[21] Koelzer, Viktor H, Korsuk Sirinukunwattana, Jens Rittscher, and Kirsten D Mertz. 2019. “Precision Immunoprofiling by Image Analysis and Artificial Intelligence.” Virchows Archiv 474: 511–22.
[22] Sajjad, Rabia, Muhammad Faheem Khan, Asif Nawaz, Malik Taimur Ali, and Muhammad Adil. 2022. “Systematic Analysis of Ovarian Cancer Empowered with Machine and Deep Learning: A Taxonomy and Future Challenges.” Journal of Computing \& Biomedical Informatics 3 (02): 64–87.
[23] Veiga-Canuto, Diana, Leonor Cerdá Alberich, Mat\’\ias Fernández-Patón, Ana Jiménez Pastor, Jose Lozano-Montoya, Ana Miguel Blanco, Blanca de las Heras, Cinta Sangüesa Nebot, PRIMAGE Project consortium, and Luis Mart\’\i-Bonmat\’\i. 2023. “Imaging Biomarkers and Radiomics in Pediatric Oncology: A View from the PRIMAGE (PRedictive In Silico Multiscale Analytics to Support Cancer Personalized DiaGnosis and Prognosis, Empowered by Imaging Biomarkers) Project.” Pediatric Radiology, 1–9.
[24] Benda, Brent B. 2006. “Survival Analyses of Social Support and Trauma among Homeless Male and Female Veterans Who Abuse Substances.” American Journal of Orthopsychiatry 76 (1): 70–79.
[25] Rota, Francesco, and Maria Cinzia Luisa Talamo. 2020. “Proactive Maintenance Strategy Based on Resilience Empowerment for Complex Buildings.” New Metropolitan Perspectives, 239.
[26] Navaz, Alramzana Nujum, Mohamed Adel Serhani, Hadeel T El Kassabi, and Ikbal Taleb. 2023. “Empowering Patient Similarity Networks through Innovative Data-Quality-Aware Federated Profiling.” Sensors 23 (14): 6443.
