An IoT Framework for the Detection of Lung Cancer Using a Decision Support System

Cancer remains an ongoing global health challenge, necessitating the progress of innovative techniques for early detection and risk assessment. In this study, a comprehensive method is presented for predicting lung cancer by utilizing a carefully curated dataset consisting of 1000 individuals from the Kaggle dataset. Cutting-edge machine learning models were leveraged, including Support Vector Machines (SVM), Naïve Bayes Multinomial (NBM), KNN, PART (Partial Rule-based Tree), and Random Forest (RF), to improve the precision of our forecasts. The dataset that was compiled included 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, real-time and continuous health monitoring was enabled, facilitating a dynamic assessment of lung cancer risk. The findings revealed that the PART model achieved an impressive accuracy of 91%, surpassing other models like Random Forest (80%), K-Nearest Neighbors (83%), SVM (89%), and Naïve Bayes Multinomial (86%). This innovative approach shows promise in the early detection of lung cancer and the provision of personalized risk assessments, possibly resulting in better patient outcomes and decreased healthcare challenges.