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| A Machine Learning Approach for Lung Cancer Detection
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Yousuf Sk, Joydeep Mukherjee,
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Abstract:
Early detection of lung cancer plays a pivotal role in improving patient outcomes and reducing mortality rates. This paper presents a novel approach utilizing machine learning techniques for the early detection of lung cancer based on symptom-based numerical attributes. Leveraging a dataset comprising numerical attributes extracted from symptoms such as Yellow fingers, Anxiety, Chronic Disease, Fatigue, Allergy, Wheezing, Coughing, Shortness of Breath, Swallowing Difficulty, and Chest pain, our proposed model aims to accurately classify individuals at risk of lung cancer. Through rigorous experimentation and validation, our study demonstrates the efficacy of machine learning algorithms in accurately identifying potential cases of lung cancer at an early stage. The findings underscore the potential of integrating machine learning with symptom-based datasets for enhanced lung cancer detection, thereby contributing to improve prognosis and patient care. Here I have used four type of machine learning algorithm -Logistic Regression, MLPC, ANN, SVM and made a comparison between them to select the best model for detection of lung cancer.
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