Innovative Approach for Early Detection and Risk Assessment of Lung Cancer Using Machine Learning Techniques

Lung cancer remains an ongoing global health challenge, necessitating the development of innovative methods for early detection and risk assessment. This study presents a comprehensive approach for predicting lung cancer by utilizing a carefully curate dataset consisting of 1000 individuals from Kaggle dataset. The cutting-edge machine learning models, including Support Vector Machines (SVM), Naive Bayes Multinomial (NBM), Meta Bagging, PART and Random Forest (RF are used to enhance the accuracy of our predictions. The dataset we’ve compiled includes a wide array of patient characteristics, encompassing demographics, lifestyle factors, medical history, and health data gathered through IoT devices. By harnessing the capabilities of IoT technology, it is possible to enable real-time and continuous health monitoring, facilitating a dynamic assessment of lung cancer risk. The finding of this study reveal that the PART model achieves an impressive accuracy of 95%, surpassing other models like NBM (67%), RF (93%), SVM (92%), and Bagging (86%). This innovative approach shows promise in the early detection of lung cancer and the provision of personalized risk assessments, potentially leading to improved patient outcomes and reduced healthcare burdens.