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Related Experiment Videos

Hybrid Modelling of Pulmonary Cancer Risk Prediction Using Classical Algorithms to Modern Machine Learning

Seeta Devi1, Roshan Yadav2, Joyce Robert Mathivanan3

  • 1Symbiosis College of Nursing (SCON), Symbiosis International (Deemed University), Maharashtra, India.

Asian Pacific Journal of Cancer Prevention : APJCP
|May 22, 2026
PubMed
Summary

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Machine learning models accurately predict pulmonary carcinoma using electronic medical records. Support Vector Machine (SVM) shows significant potential for early cancer detection and personalized screening in healthcare.

Area of Science:

  • Oncology
  • Data Science
  • Medical Informatics

Background:

  • Early diagnosis of pulmonary cancer remains a significant clinical challenge, contributing to high mortality rates.
  • Data-driven prediction models are crucial for improving early detection and patient outcomes.
  • Machine learning (ML) offers a promising approach for developing these predictive models.

Purpose of the Study:

  • To predict pulmonary cancer utilizing hybrid machine learning models.
  • To evaluate the efficacy of various ML algorithms for early pulmonary carcinoma detection.
  • To identify key predictors for pulmonary cancer from electronic medical records (EMRs).

Main Methods:

  • A comprehensive review of ML algorithms was conducted using EMR data from 1000 patients.
Keywords:
Deep LearningEnsembleMachine LearningPulmonary carcinomaprediction

Related Experiment Videos

  • Data pre-processing included label correction, multicollinearity assessment, and dimensionality reduction.
  • Eighteen significant features were identified, and ML models (SVM, RF, LR, DL) were trained and evaluated using standard performance metrics.

    • Main Results:

    • Chi-square tests identified age, passive smoking, obesity, smoking, and coughing blood as significant predictors (p<0.001).
    • Ensemble models, including SVM, RF, and LR, achieved perfect classification scores (accuracy, precision, recall, F1, AUC = 1.000).
    • Deep Learning (DL) and SVM Bagging demonstrated high accuracy (97%), with Neural Networks (NN) and Multilayer Perceptrons (MLP) performing well above 96%.

    Conclusions:

    • ML, particularly SVM, shows strong potential for early pulmonary carcinoma prediction using EMR data.
    • The findings support integrating ML tools into clinical workflows for data-driven, personalized cancer screening.
    • These advancements can enhance decision-making in healthcare for improved patient management.