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A Framework for Lung Cancer Detection Using Machine Learning

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Part of the Lecture Notes in Electrical Engineering book series (LNEE,volume 914)


Cancer is a lethal disease that has been ruling the world for the death of millions of people every year. It causes abnormal and uncontrollable growth of cells and destroys the body tissues and so similarly, in lung cancer, cells in the lungs grow uncontrollably. So, the detection of lung cancer is a must as we also know that the earlier the detection, the survival rate of the victim also increases accordingly. In many cases, cancer is detected lately and the chances to save the patient’s life also diminishes. The figure of persons diagnosed with lung cancer is directly proportionate to the figure of chain smokers and other hazardous addictions. The prediction of cancer and its prognosis has been a tedious task for doctors and medical workers since cancer’s discovery. Among the various applied technologies, supervised machine learning is a very relevant option in which a particular model is trained to work on the complex datasets, containing different features, and then the prediction is made accordingly. There will be the usage of different techniques and algorithms (like K-nearest neighbors, decision trees, naïve Bayes, kernelized support vector machines, and logistic regression) to work on the labeled datasets. Adding up, the model basically focuses on two foci: cancer susceptibility and model accuracy.


  • Cancer detection
  • Supervised machine learning
  • Labeled datasets
  • KNN
  • Logistic regression
  • Naïve Bayes
  • Support vector machine

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  • DOI: 10.1007/978-981-19-2980-9_17
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Correspondence to Aakash Nakarmi .

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Nakarmi, A., Sagar, A.K., Musharaf, S., Jahangir, H. (2022). A Framework for Lung Cancer Detection Using Machine Learning. In: Shaw, R.N., Das, S., Piuri, V., Bianchini, M. (eds) Advanced Computing and Intelligent Technologies. Lecture Notes in Electrical Engineering, vol 914. Springer, Singapore.

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