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All Journal Journal of Software Engineering Ampera Journal Software, Hardware and Information Technology Journal Computer Science and Informatic Systems : J-Cosys MDP Student Conference Jurnal Software Engineering and Computational Intelligence Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Media Journal of General Computer Science (MJGCS) Journal of Intelligent Systems and Information Technology Jurnal Sistem Informasi Komputer dan Teknologi Informasi
Firza Septian
Universitas Serelo Lahat
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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Risk Factors to Diagnosis A Data-Driven Model for Lung Cancer Prediction using BES-DT Firza Septian; Muhammad Sulkhan Nurfatih
Journal of Software Engineering Ampera Vol. 7 No. 2 (2026): Journal of Software Engineering Ampera
Publisher : APTIKOM SUMSEL

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Abstract

Lung cancer remains one of the most critical health challenges worldwide, requiring accurate and interpretable predictive models to support early diagnosis. This study proposes a novel framework by integrating the Bald Eagle Search (BES) optimization algorithm with the Decision Tree (DT) classifier, forming the BES-DT model. The dataset, consisting of demographic, behavioral, and clinical risk factors, was preprocessed and optimized using BES to select the most informative features while reducing redundancy. BES, inspired by the hunting behavior of bald eagles, balances exploration and exploitation to identify optimal solutions, enabling DT to construct transparent decision rules for clinical interpretation. Experimental evaluation demonstrated that BES-DT achieved superior performance compared to the baseline DT, with accuracy of 92.6%, precision of 98.8%, recall of 93.0%, F1-score of 95.8%, and ROC-AUC of 0.90, confirming strong discriminative ability. These findings highlight that BES-DT not only improves predictive accuracy but also maintains interpretability, offering a balanced solution for medical decision support. In conclusion, BES-DT shows significant potential in advancing early detection strategies for lung cancer.
Co-Authors Agustian Prakarsya Arief Zikry Ema Utami Iski Mediansyah Kusrini Mubarok, M. Husni Muhammad Bitrayoga Muhammad Sulkhan Nurfatih Nina Dwi Putriani Prakarsya, Agus Prakarsya, Agustian TONNY HIDAYAT Yusi Nurmala Sari Yusi Nurmalasari