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Predicting mortality of cancer patients using artificial intelligence, patient data and blood tests

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Abstract

Several authors have shown that hematological parameters can be used to detect poor prognosis in patients with cancer. Thus, such features could be used in artificial intelligence (AI-based) models to predict mortality among these patients. This work aimed to develop and compare several AI-based models to predict the prognosis (death vs. survival) of cancer in patients using blood tests and patient data as inputs. At total, 908 cancer patients were assisted in a prospective study. Four artificial intelligence models were compared: artificial neural networks (ANN), supporting vector machines (SVM), decision trees and neuro-fuzzy networks. Also, four different input strategies were tested, considering the use of 49, 45, 22 and 14 inputs. The results of this study showed that the ANN and the SVM presented the best results, using 45 inputs. The ANN was the best model since it presented better statistical values for the positive (death) and negative (survival) classes. The use of blood parameters as inputs for AI-based models could be used to predict death in patients with cancer, and this methodology can be expanded to other diseases.

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Acknowledgements

São Paulo Research Foundation (FAPESP)—(#2016/14172-6).

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Authors and Affiliations

  1. Department of Chemical Engineering, Federal University of São Paulo (UNIFESP), Diadema, São Paulo, Brazil

    Tiago D. Martins & Gabriel Y. Ottaiano

  2. School of Chemical Engineering, University of Campinas (UNICAMP), Campinas, São Paulo, Brazil

    Tiago D. Martins, Rubens Maciel-Filho & Mohamad Al Bannoud

  3. School of Medical Science, FCM, University of Campinas (UNICAMP), Campinas, Brazil

    Gabriele S. S. Gois, Thaizy R. A. Anhaia & Joyce M. Anninchino-Bizzacchi

  4. Laboratory of Hemostasis, Hematology and Transfusion Medicine Center, Hemocentro – University of Campinas (UNICAMP), Campinas, Brazil

    Silmara A. L. Montalvão, Millene E. A. Almeida, Monique R. M. Ferreira, Beatriz M. Martinelli, Maria C. G. L. Fernandes, Stephany C. Huber & Joyce M. Anninchino-Bizzacchi

  5. Department of Obstetrics and Gynecology. Division of Oncology, Women’s Hospital – CAISM – University of Campinas (UNICAMP), Campinas, Brazil

    Júlio C. Teixeira

  6. Clinical Hospital of UNICAMP, Campinas, Brazil

    José B. C. Carvalheira, Carmen S. P. Lima, Nelson A. Andreollo, Maurício Etchebehere, Lair Zambon, Ubirajara Ferreira, Alfio J. Tincani, Antônio S. Martins, Cláudio S. R. Coy, José C. T. Seabra, Ricardo K. Mussi & Helder Tedeschi

  7. Clinical Hospital of UFMG, Belo Horizonte, Brazil

    Daniel Ribeiro

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  1. Tiago D. Martins
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Correspondence to Tiago D. Martins.

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This study was approved by the ethical committee of the University of Campinas (CAAE: 87212317.7.0000.5404).

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Martins, T.D., Maciel-Filho, R., Montalvão, S.A.L. et al. Predicting mortality of cancer patients using artificial intelligence, patient data and blood tests. Neural Comput & Applic 36, 15599–15616 (2024). https://doi.org/10.1007/s00521-024-09915-4

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