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A comparative analysis of classical machine learning and deep learning techniques for predicting lung cancer survivability

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Abstract

Lung cancer, one of the deadliest forms of cancer, can significantly improve patient survival rates by 60–70% if detected in its early stages. The prediction of lung cancer patient survival has grown to be a popular area of research among medical and computer science experts. This study aims to predict the survival period of lung cancer patients using 12 demographic and clinical features. This is achieved through a comparative analysis between traditional machine learning and deep learning techniques, deviating from previous studies that primarily used CT or X-ray images. The dataset included 10,001 lung cancer patients, and the data attributes involved gender, age, race, T (tumor size), M (tumor dissemination to other organs), N (lymph node involvement), Chemo, DX-Bone, DX-Brain, DX-Liver, DX-Lung, and survival months. Six supervised machine learning and deep learning techniques were applied, including logistic-regression (Logistic), Bayes classifier (BayesNet), lazy-classifier (LWL), meta-classifier (AttributeSelectedClassifier (ASC)), rule-learner (OneR), decision-tree (J48), and deep neural network (DNN). The findings suggest that DNN surpassed the performance of the six traditional machine learning models in accurately predicting the survival duration of lung cancer patients, achieving an accuracy rate of 88.58%. This evidence is thought to assist healthcare experts in cost management and timely treatment provision.

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Data availability

The data were collected from the SEER database.

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Authors and Affiliations

  1. Department of Cell Biology of National Translational Science Center for Molecular Medicine and Department of Radiation Oncology of Xijing Hospital, Fourth Military Medical University, Xi’an, China

    Shigao Huang

  2. Department of Software Engineering, Bandirma Onyedi Eylul University, Balikesir, Turkey

    Ibrahim Arpaci

  3. Faculty of Engineering & IT, The British University in Dubai, Dubai, United Arab Emirates

    Mostafa Al-Emran

  4. Department of Computer Techniques Engineering, Dijlah University College, Baghdad, Iraq

    Mostafa Al-Emran

  5. Department of Informatics, Tokat Gaziosmanpasa University, Tokat, Turkey

    Serhat Kılıçarslan

  6. Institute of Informatics and Computing in Energy, Universiti Tenaga Nasional, Putrajaya Campus, 43000, Kajang, Selangor, Malaysia

    Mohammed A. Al-Sharafi

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  1. Shigao Huang
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  2. Ibrahim Arpaci
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  4. Serhat Kılıçarslan
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  5. Mohammed A. Al-Sharafi
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Correspondence to Mostafa Al-Emran.

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Huang, S., Arpaci, I., Al-Emran, M. et al. A comparative analysis of classical machine learning and deep learning techniques for predicting lung cancer survivability. Multimed Tools Appl 82, 34183–34198 (2023). https://doi.org/10.1007/s11042-023-16349-y

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