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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Tikrit University
2025-08-01
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| Series: | Tikrit Journal of Engineering Sciences |
| Subjects: | |
| Online Access: | http://www.tj-es.com/ojs/index.php/tjes/article/view/2527 |
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| Summary: | 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.
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| ISSN: | 1813-162X 2312-7589 |