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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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2024-11-01
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| Series: | Discover Artificial Intelligence |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44163-024-00204-6 |
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| Summary: | 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. |
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| ISSN: | 2731-0809 |