International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Machine Learning-driven Lung Cancer Detection

Author(s) PRIYANSHU SAHAY, DEEPANSHU GUPTA, PIYUSH KUMAR, MEDHAVI PANDEY
Country India
Abstract Lung cancer remains one of the most prevalent and deadly forms of cancer worldwide, highlighting the critical need for early detection methods to improve patient outcomes. In this study, we present a novel approach to lung cancer detection utilizing machine learning techniques, specifically leveraging the Random Forest algorithm. Our research focuses on developing a robust and accurate model capable of detecting lung cancer from medical imaging data with high precision and sensitivity. We collected a comprehensive dataset consisting of radiographic images from patients with confirmed lung cancer diagnoses, as well as from healthy individuals for comparison. The Random Forest algorithm, known for its versatility and ability to handle complex datasets, was employed as the primary machine learning framework in our study. Through a process of feature extraction and selection, our model was trained to identify patterns and subtle abnormalities indicative of lung cancer within the imaging data. We conducted extensive experimentation and evaluation to assess the performance of our proposed approach. Our results demonstrate promising outcomes, with the Random Forest model achieving notable accuracy rates in distinguishing between cancerous and non-cancerous lung tissues. Additionally, the model exhibited favorable sensitivity and specificity metrics, indicating its potential as a reliable tool for early lung cancer detection. Furthermore, we conducted comparative analyses with other machine learning algorithms commonly utilized in medical image analysis tasks. The findings underscore the efficacy of the Random Forest algorithm in this particular domain, showcasing its superiority in terms of both performance and interpretability. In conclusion, our research contributes to the ongoing efforts aimed at improving lung cancer diagnosis through the integration of machine learning technologies. By harnessing the power of the Random Forest algorithm, we offer a promising solution for early detection, thereby facilitating timely interventions and ultimately enhancing patient outcomes in the fight against lung cancer.
Keywords Lung cancer detection, machine learning, Random Forest algorithm, medical imaging data, feature extraction, model evaluation, sensitivity, specificity, comparative analysis, interpretability, early detection, patient outcomes.
Field Engineering
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-04-29
Cite This Machine Learning-driven Lung Cancer Detection - PRIYANSHU SAHAY, DEEPANSHU GUPTA, PIYUSH KUMAR, MEDHAVI PANDEY - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.18531
DOI https://doi.org/10.36948/ijfmr.2024.v06i02.18531
Short DOI https://doi.org/gtsnv3
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E-ISSN 2582-2160
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