Browsing by Author "Hasan, Km. Zayedul"

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    A Machine Learning Approach for Early Detection and Improved Decision-Making for Lung Cancer Diagnosis
    (Daffodil International University, 2024-02-03) Hasan, Km. Zayedul
    Lung cancer is presently the leading cause of cancer-related mortalities worldwide. Environmental conditions, lifestyle habits, and genetics are the main causes of lung cancer. Early detection of lung cancer is pivotal in preventing its severe consequences. The integration of machine learning algorithms in the healthcare industry has led to significant advancements in disease diagnosis. These algorithms help medical professionals diagnose lung cancer accurately in the early stages. In this study, we propose using Quadratic Discriminant Analysis to improve the accuracy of lung cancer diagnosis by analyzing the symptoms of lung cancer patients. Our proposed technique is more suitable for diagnosing lung cancer with higher accuracy and precision compared to previous techniques. The methodology has demonstrated an impressive overall accuracy of 98% based on empirical results.
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    A Machine Learning Approach for Early Detection and Improved Decision-Making for Lung Cancer Diagnosis
    (DAFFODIL INTERNATIONAL UNIVERSITY, 2024-02-05) Hasan, Km. Zayedul
    Lung cancer is presently the leading cause of cancer-related mortalities worldwide. Environmental conditions, lifestyle habits, and genetics are the main causes of lung cancer. Early detection of lung cancer is pivotal in preventing its severe consequences. The integration of machine learning algorithms in the healthcare industry has led to significant advancements in disease diagnosis. These algorithms help medical professionals diagnose lung cancer accurately in the early stages. In this study, we propose using Quadratic Discriminant Analysis to improve the accuracy of lung cancer diagnosis by analyzing the symptoms of lung cancer patients. Our proposed technique is more suitable for diagnosing lung cancer with higher accuracy and precision compared to previous techniques. The methodology has demonstrated an impressive overall accuracy of 98% based on empirical results.