A Meta-Ensemble Predictive Model For The Risk Of Lung Cancer

Authors

Sideeqoh Oluwaseun Olawale-Shosanya, Department of Computer Science, Tai Solarin University of Education, Ijagun, Ogun State, NigeriaFollow
Olayinka Olufunmilayo Olusanya, Department of Computer Science, Tai Solarin University of Education, Ijagun, Ogun State, Nigeria
Adeyemi Omotayo Joseph, Department of Computer Science, Tai Solarin University of Education, Ijagun, Ogun State, Nigeria
Kabir Oluwatobi Idowu, Department of Mathematics, Purdue University, West Lafayette, IN 47907, USA
Oyelade Babatunde Eriwa, Department of Mathematics and Statistics, College of Arts and Sciences, Bowling Green State University Ohio, United States
Adedeji Oladimeji Adebare, Department of Computer Science, Tai Solarin University of Education, Ijagun, Ogun State, Nigeria
Morufat Adebola Usman, Department of Computer Science, Tai Solarin University of Education, Ijagun, Ogun State, Nigeria

Abstract

The lungs play a vital role in supplying oxygen to every cell, filtering air to prevent harmful substances, and supporting defense mechanisms. However, they remain susceptible to the risk of diseases such as infections, inflammation, and cancer that affect the lungs. Meta-ensemble techniques are prominent methods used in machine learning to enhance the accuracy of classifier learning systems in making predictions. This work proposes a robust predictive model using a meta-ensemble method to identify high-risk individuals with lung cancer, thereby taking early action to prevent long-term problems benchmarked upon the Kaggle Machine Learning practitioners' Lung Cancer Dataset. Three machine learning ensemble models—Random Forest, Adaptive Boosting (AdaBoost), and Gradient Boosting—were used to develop the meta-ensemble models proposed in this paper, whereby the three ensemble models were adopted as base classifiers while one of them was adopted as the meta-classifier. In addition, two of the ensemble models were used as base classifiers, while the third was used as a meta-classifier to evaluate lung cancer risk prediction. Different graphs were evaluated to show that people with these features are liable to develop lung cancer. The proposed model has immensely improved prediction performance. The meta-ensemble models were simulated using the Python simulation environment, and the 5-fold cross-validation technique was used. The model validation was carried out using several known performance evaluation methodologies. The results of the experiments showed that gradient boosting achieved a maximum accuracy of 100%, an area under the curve (AUC), and a precision of 100%. The proposed model was compared with novel machine learning methods and popular state-of-the-art (SOTA) deep learning techniques. It was confirmed from the results that the model in this study had the best accuracy at lung cancer risk prediction. This study's results can be utilized to enhance the performance of actual patient risk prediction systems in the future.

Recommended Citation

Olawale-Shosanya, Sideeqoh Oluwaseun; Olusanya, Olayinka Olufunmilayo; Joseph, Adeyemi Omotayo; Idowu, Kabir Oluwatobi; Eriwa, Oyelade Babatunde; Adebare, Adedeji Oladimeji; and Usman, Morufat Adebola (2024) "A Meta-Ensemble Predictive Model For The Risk Of Lung Cancer," Al-Bahir Journal for Engineering and Pure Sciences: Vol. 5: Iss. 1, Article 4.
Available at: https://doi.org/10.55810/2313-0083.1068

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