A Study on Machine Learning Algorithms for Lung Cancer Classification and Prediction

• Published in: 2022 8th International Conference on Signal Processing and Communication (ICSC) pp. 15 - 20
• Main Authors: Koppisetti, Sai Sri Ramya, Chaitanya, Sistla Sri Sai Venkata, Rani, T. Sudha, Kiruthika Devi, B. S.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-12-2022
• Subjects: Bayes methods; Enhanced K-nearest neighbor; Image Processing; Lung cancer; Machine learning; Machine learning algorithms; Multinomial Bayesian; Neural Networks; Prediction methods; Prognostics and health management; Signal processing; SVM; Thresholding
• ISSN: 2643-444X
• DOI: 10.1109/ICSC56524.2022.10009171

Machine learning (ML) is an AI subfield that helps programs improve their predictive abilities without being explicitly taught to do so. This ML incorporates a number of prediction methods, and it makes use of past outcomes to calculate predicted values for the future. Lung cancer is the biggest cancer killer, accounting for a disproportionately large number of cancer-related deaths. Unchecked cell growth leads to tumor development. The cancerous tumor multiplies and metastasizes throughout the body. Worldwide, lung cancer is the leading cause of death, accounting for around 7.6 million deaths annually. No symptoms are apparent until the disease has progressed significantly. Patients with this cancer sometimes don't seek medical help until they've already gone past the point of no return. If cancer is detected and treated when still in its early stages, survival rates can increase by 47%. Worldwide mortality now sits at 17%. Although the causes of this malignancy are not fully understood, it is clear that accurate diagnosis and early prognosis are crucial for lowering mortality rates. In this paper, we discuss several methods for diagnosing lung cancer ahead of time.