Abstract
The machine learning is essentially the computer assessment inevitably developed over practice which is a part within the sphere of artificial intelligence. Machine learning algorithms are based upon models that are data driven which is the training set provided to the algorithm resulting in making prediction. Statistical, probabilistic knowledge is used to site the patterns from past examples which contain data sets which are huge, defeating, or hard. This skill is explicitly acceptable for medical applications especially for cancer detection and diagnosis as they rely on complex measurements of proteomics and genomics. The application of machine learning in cancer prediction is around 20 years now. The accuracy of the algorithms has gradually increased from past till now. The intention of this paper is to evaluate various machine learning algorithms particularly for the lung cancer detection to look for a void in the future improvement of lung cancer detection. Each technique is analyzed, and the overall disadvantages are pointed out. Various types of machine learning algorithms like Naive Bayes, support vector machine (SVM), logistic regression, and artificial neural network (ANN) have been applied in the healthcare sector for analysis and prognosis of lung cancer.
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Jaweed, A., Siddiqui, F. (2022). Implementation of Machine Learning in Lung Cancer Prediction and Prognosis: A Review. In: Tavares, J.M.R.S., Dutta, P., Dutta, S., Samanta, D. (eds) Cyber Intelligence and Information Retrieval. Lecture Notes in Networks and Systems, vol 291. Springer, Singapore. https://doi.org/10.1007/978-981-16-4284-5_20
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DOI: https://doi.org/10.1007/978-981-16-4284-5_20
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