###### Abstract  
Lung cancer remains a leading cause of global mortality, with early detection being critical for improving the patient survival rates. However, applying machine learning and deep learning effectively for lung cancer prediction using symptomatic and lifestyle data requires the careful consideration of feature selection and model optimization, which is not consistently addressed in the existing research. This research addresses this gap by systematically evaluating and comparing the predictive efficacy of several machine learning and deep learning models, employing rigorous data preprocessing, including feature selection with [PERSON]'s correlation, outlier removal, and normalization, on a patient symptom and lifestyle factor dataset from Kaggle. Machine learning classifiers, including Decision Trees, K-Nearest Neighbors, Random Forest, Naive Bayes, AdaBoost, Logistic Regression, and Support Vector Machines, were implemented using Weka simultaneously with neural network models with 1, 2, and 3 hidden layers, which were developed in Python within a Jupyter Notebook environment. The model performance was assessed using K-fold cross-validation and 80/20 train/test splitting. The results highlight the importance of feature selection for enhancing the model accuracy and demonstrate that the single-hidden-layer neural network, trained for 800 epochs, achieved a prediction accuracy of 92.86%, outperforming the machine learning models. This study contributes to developing more effective computational methods for early lung cancer detection, ultimately supporting improved patient outcomes. l 2025 15 4507 17 April 2025 18 April  
soil, and water, is the second primary cause of lung cancer. It may infiltrate buildings via fissures and may induce lung cancer after prolonged inhalation [1]. Radiation treatment, atypical dietary habits, and familial genetic predisposition also cause lung cancer-related fatalities. The literature emphasizes that risk prediction is of greater importance than clinical evaluation in lung cancer screening [1; 2; 3; 5]. Although medical technology has made strides, lung cancer persists as a significant health challenge, mainly due to complications of early diagnosis [6]; in fact, delayed detection often reduces the treatment effectiveness and leads to poorer patient outcomes [6; 7]. The literature has used several mathematical models to detect and prevent diseases, facilitate timely treatment interventions, and improve the health outcomes [8; 9]. Early detection, before metastasis, significantly enhances the possibility of effective treatment in lung cancer cases, as early cancer detection and risk factor assessment enable the administration of appropriate therapies and preventive measures [7]. The evidence demonstrates that the inherent difficulties in early lung cancer detection result in delayed diagnoses beyond six months, substantially diminishing the survival rates, and render treatment significantly more challenging [7; 8; 10]. In the last few years, machine learning (ML) and data mining have become reliable and essential tools in the medical field due to their ability to reveal hidden patterns in the data, improving the decision-making accuracy [8; 9]. Researchers have utilized ML and soft computing techniques to effectively identify many types of cancer at early stages using classification methods [7; 8; 11] and developed advanced models for predicting cancer therapy outcomes at the onset of treatment [6; 7]. However, selecting an appropriate learning algorithm is critical for precisely diagnosing lung cancer and understanding its relation to patient habits [3; 5]. ML automates disease prediction, while data mining integrates ML, statistics, and database techniques to preprocess and extract meaningful patterns from large datasets [8; 11]. Therefore, it is critical to develop and refine computational methods that can accurately predict the lung cancer risk and facilitate timely intervention.