Integrating Features and Unlabeled Data with Modified Support Vector Machines for Improved Lung Cancer Detection - G. N. K. Suresh Babu, Suman Antony Lasrado (International Journal of Intelligent Systems and Applications in Engineering, 2024)
This research explores the application of Modified Support Vector Machines (MSVMs) as a potent classifier for the effective diagnosis of lung cancer, aiming to enhance the accuracy and performance compared to conventional Support Vector Machines (SVMs). While SVMs have been widely employed, their limitation lies in treating all features equally, potentially affecting the precision of disease detection. In response to this, MSVMs introduce a novel approach by incorporating both labeled and unlabeled data into the learning process, gradually searching for the optimal separating hyper plane. The key innovation lies in the assignment of weights to a kernel function, measuring the importance of individual features and addressing the shortcomings of traditional SVMs. By acknowledging the varying significance of features, MSVMs offer a more explored and efficient classification process. The newly formulated kernel function enables the integration of labeled and unlabeled data, contributing to a more robust learning model. The proposed modification not only enhances the classifier’s ability to discern between malignant and benign lung tissues but also opens avenues for improved pattern recognition indicative of lung cancer. The research investigates the comparative performance of MSVMs against different SVMs, with preliminary results indicating promising outcomes. The integration of both labeled and unlabeled data, combined with the consideration of feature importance through weighted kernel functions, demonstrates the potential of MSVMs as a breakthrough in the accurate classification of lung cancer. While further validation with larger datasets is essential, this study suggests that MSVMs could emerge as a significant advancement in the field of lung cancer diagnosis, offering heightened 93% accuracy and 99% specificity in predicting and classifying the lung cancer disease.