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All Journal International Journal of Advances in Artificial Intelligence and Machine Learning
Muhammad Hafiz Kurniawan
Universitas Sriwijaya, Palembang
Computer Science & IT

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A Comparative Evaluation of Predictive Models for Lung Cancer: Insights from Logistic Regression, Naive Bayes, and Random Forest Muhammad Hafiz Kurniawan; Misinem Misinem
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 2 No. 1 (2025): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v2i1.378

Abstract

This study aims to evaluate the performance of three machine learning models-Logistic Regression, Naive Bayes, and Random Forest-in predicting lung cancer using a publicly available dataset from Kaggle. The data used included demographic information, risk factors, and diagnostic imaging features, with significant class imbalance between benign and malignant cases. To address this imbalance, the Synthetic Minority Sampling Technique (SMOTE) was applied. In addition, Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE) were used for dimensionality reduction and feature selection to improve model performance. The results showed that Random Forest, especially when combined with PCA, outperformed the other models with the highest accuracy of 96.77% and a balanced F1 score of 0.50 for the minority class. Although Logistic Regression achieved high accuracy, it was less effective in predicting minority classes, especially when combined with RFE. Meanwhile, Naive Bayes showed moderate performance but was limited by the assumption of feature independence. The application of SMOTE significantly improved the model's ability to handle class imbalance, while PCA proved more effective than RFE in improving model performance. This study highlights the importance of selecting appropriate machine learning models and preprocessing techniques for lung cancer prediction. Random Forest, with its ability to model complex relationships and handle imbalanced data, emerged as the most effective model for this task. These findings underscore the potential of machine learning in medical diagnostics and provide valuable insights for future research.
The Eye's Signature: Innovative Approaches to Iris Detection Dhidhi Pambudi; Fadly Fadly; Muhammad Hafiz Kurniawan; Haryanto Haryanto
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 2 No. 1 (2025): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v2i1.379

Abstract

This research aims to develop and evaluate a deep learning-based iris detection system using a specialized Convolutional Neural Network (CNN) architecture. The research methodology includes data set preprocessing, CNN model design, training using Adam optimization, as well as evaluation using accuracy, precision, recall, and F1 score metrics. The dataset used was obtained from Kaggle and preprocessed before being divided into training, validation, and testing sets. The CNN model consists of three convolutional layers with increasing filter sizes (32, 64, and 128), ReLU activation, batch normalization, and MaxPooling layers for efficient feature extraction, as well as dropout regularization to reduce overfitting. Experimental results show that the proposed model achieves a high classification accuracy of 97.33%, with robust performance against variations and noise in iris images. Comparative analysis with traditional iris recognition methods confirms the superiority of deep learning in handling challenges such as lighting changes and occlusions. Although the results are promising, challenges such as data bias and computational demands are still a concern. Future research will explore more advanced architectures as well as additional pre-processing techniques to improve the generalizability and effectiveness of the system in real-world applications.
Co-Authors Dhidhi Pambudi Fadly Fadly Haryanto Haryanto Misinem Misinem