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Neural Network-Based Cancer Diagnosis from Routine Hematological and Biochemical Data: Performance, Target Leakage,

Jehad F Alhmoud1, Moath Alqaraleh2, Futoon Abedrabbu Al-Rawashdea2

  • 1Department of Medical Laboratory Sciences, Jordan University of Science and Technology, Irbid, Jordan.

Acta Informatica Medica : AIM : Journal of the Society for Medical Informatics of Bosnia & Herzegovina : Casopis Drustva Za Medicinsku Informatiku Bih
|February 2, 2026
PubMed
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Routine blood tests and tumor markers offer limited value for cancer prediction in patients already undergoing specialist evaluation. Hemoglobin levels were lower in cancer patients but were not a specific diagnostic indicator.

Keywords:
Cancer diagnosisneural networksrisk stratificationroutine laboratory teststarget leakage

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Area of Science:

  • Oncology
  • Medical Diagnostics
  • Machine Learning in Healthcare

Background:

  • Routine blood tests and tumor markers are common in oncology workups for potential malignancy.
  • Machine learning (ML) approaches are increasingly explored for automated cancer prediction using these data.
  • The utility of these familiar markers for prediction within specialist pathways and the risk of target leakage require careful consideration.

Purpose of the Study:

  • To investigate the relationship between basic demographic, hematological, biochemical, and clinical variables and cancer diagnosis in a cancer-enriched cohort.
  • To evaluate the implications of these relationships for neural network-based cancer prediction models.

Main Methods:

  • A secondary, analytical cross-sectional study was conducted using 1,000 cases from the Cancer Risk Stratification Using Lab Parameters dataset.
  • Data extracted included demographics, smoking status, family history, complete blood count, blood glucose, tumor markers (CA-125, PSA, CEA), cancer stage, and survival.
  • Neural network analysis was performed to assess cancer status (cancer vs. no cancer).

Main Results:

  • The cohort comprised over 80% of patients with a malignant diagnosis.
  • Most routine laboratory and biochemical values did not significantly differ between cancer and non-cancer groups, remaining within conventional reference ranges.
  • Hemoglobin levels were modestly but significantly lower in cancer patients; cancer status strongly correlated with stage and weakly with survival.

Conclusions:

  • In a referred, cancer-enriched population, clinical risk factors and individual routine laboratory parameters provided minimal additional diagnostic discrimination beyond the presence of a cancer stage.
  • Hemoglobin served as a non-specific indicator of general illness rather than a specific diagnostic marker for cancer.
  • Careful modeling is needed to avoid target leakage when using these variables for ML-based cancer prediction.