ML-based early detection of lung cancer: an integrated and in-depth analytical framework
Abstract The human lungs, crucial for supplying oxygen, are vulnerable to diseases such as lung cancer, a leading cause of mortality. Timely prediction of lung cancer is essential to enable early intervention by healthcare professionals, enhancing patient outcomes and saving lives. This study introd...
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| Format: | Article |
| Language: | English |
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Springer
2024-11-01
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| Series: | Discover Artificial Intelligence |
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| Online Access: | https://doi.org/10.1007/s44163-024-00204-6 |
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| _version_ | 1846147563443453952 |
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| author | Yusupha Sinjanka Veerpal Kaur Usman Ibrahim Musa Karandeep Kaur |
| author_facet | Yusupha Sinjanka Veerpal Kaur Usman Ibrahim Musa Karandeep Kaur |
| author_sort | Yusupha Sinjanka |
| collection | DOAJ |
| description | Abstract The human lungs, crucial for supplying oxygen, are vulnerable to diseases such as lung cancer, a leading cause of mortality. Timely prediction of lung cancer is essential to enable early intervention by healthcare professionals, enhancing patient outcomes and saving lives. This study introduces a comprehensive Machine Learning (ML) model designed to predict lung cancer at an early stage, utilizing a dataset sourced from Kaggle. Built on the Random Forest algorithm, the model assesses a diverse set of characteristics and variables, including gender, age, and exposure to various environments and lifestyles. It accurately identifies individuals at a higher risk of developing early-stage lung cancer, facilitating prompt intervention and personalized treatment strategies. Key evaluation metrics demonstrating the model's effectiveness include precision, F1 score, recall, and accuracy. The findings indicate a model accuracy of approximately 97.9%, underscoring its potential as a valuable tool for enhancing the early detection of lung cancer. |
| format | Article |
| id | doaj-art-24b646437c9145bb81af8c57c4f96e55 |
| institution | Kabale University |
| issn | 2731-0809 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Artificial Intelligence |
| spelling | doaj-art-24b646437c9145bb81af8c57c4f96e552024-12-01T12:36:35ZengSpringerDiscover Artificial Intelligence2731-08092024-11-014111810.1007/s44163-024-00204-6ML-based early detection of lung cancer: an integrated and in-depth analytical frameworkYusupha Sinjanka0Veerpal Kaur1Usman Ibrahim Musa2Karandeep Kaur3School of Computer Science and Engineering, Lovely Professional UniversitySchool of Computer Science and Engineering, Lovely Professional UniversitySchool of Computer Applications, Lovely Professional UniversitySchool of Computer Science and Engineering, Lovely Professional UniversityAbstract The human lungs, crucial for supplying oxygen, are vulnerable to diseases such as lung cancer, a leading cause of mortality. Timely prediction of lung cancer is essential to enable early intervention by healthcare professionals, enhancing patient outcomes and saving lives. This study introduces a comprehensive Machine Learning (ML) model designed to predict lung cancer at an early stage, utilizing a dataset sourced from Kaggle. Built on the Random Forest algorithm, the model assesses a diverse set of characteristics and variables, including gender, age, and exposure to various environments and lifestyles. It accurately identifies individuals at a higher risk of developing early-stage lung cancer, facilitating prompt intervention and personalized treatment strategies. Key evaluation metrics demonstrating the model's effectiveness include precision, F1 score, recall, and accuracy. The findings indicate a model accuracy of approximately 97.9%, underscoring its potential as a valuable tool for enhancing the early detection of lung cancer.https://doi.org/10.1007/s44163-024-00204-6Lung cancerRandom forest algorithmMachine learning |
| spellingShingle | Yusupha Sinjanka Veerpal Kaur Usman Ibrahim Musa Karandeep Kaur ML-based early detection of lung cancer: an integrated and in-depth analytical framework Discover Artificial Intelligence Lung cancer Random forest algorithm Machine learning |
| title | ML-based early detection of lung cancer: an integrated and in-depth analytical framework |
| title_full | ML-based early detection of lung cancer: an integrated and in-depth analytical framework |
| title_fullStr | ML-based early detection of lung cancer: an integrated and in-depth analytical framework |
| title_full_unstemmed | ML-based early detection of lung cancer: an integrated and in-depth analytical framework |
| title_short | ML-based early detection of lung cancer: an integrated and in-depth analytical framework |
| title_sort | ml based early detection of lung cancer an integrated and in depth analytical framework |
| topic | Lung cancer Random forest algorithm Machine learning |
| url | https://doi.org/10.1007/s44163-024-00204-6 |
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