eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2025-02-20 18 March Spl Edition 85 98 10.13005/bpj/3075 64314 article Rohit Lamba 1 Pooja Rani 2 Ravi Kumar Sachdeva 3 Priyanka Bhatla 4 Karan Kumar 5 Vikas Mittal 6 Department of Electronics and Communication Engineering, MMEC, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, India MCA Department, MMICTBM, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, India Department of Computer Science and Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India Department of Computer Science and Engineering, Chandigarh University, Gharuan, Mohali, Punjab, India Department of Electronics and Communication Engineering, Chandigarh University, Gharuan, Mohali, India Department of Computer Science and Engineering, Uttaranchal Institute of Technology (UIT), Uttaranchal University, Dehradun, Uttarakhand, India Lung cancer is one of the leading causes of death worldwide. Increasing patient survival rates requires early detection. Traditional methods of diagnosis often result in late-stage detection, necessitating the development of more advanced and accurate predictive models. This paper has proposed a methodology for lung cancer prediction using machine learning models. Synthetic minority over-sampling technique (SMOTE) is used before classification to resolve the problem of class imbalance. Bayesian optimization is used to enhance model’s performance. Performance of three classifiers adaptive boosting (AdaBoost), random forest (RF), and extreme gradient boosting (XGBoost) is evaluated both with and without hyperparmater optimization. Optimized models of RF, AdaBoost and XGBoost achieved accuracies of 96.11%, 95.74% and 95.92% respectively. Results demonstrate the effectiveness of combining machine learning classifiers, SMOTE, and hyperparameter tuning in improving prediction accuracy. https://biomedpharmajournal.org/vol18marchspledition/an-optimized-predictive-machine-learning-model-for-lung-cancer-diagnosis/ AdaBoost Lung Cancer Machine Learning Random Forest XGBoost