Early Diagnosis and Prediction of Recurrent Cancer Occurrence in a Patient Using Machine Learning
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 7, Pages 6785-6794
AbstractMachine learning is the way to implement many kinds of research applications which will create a challenge to implement the novel tasks. Cancer is the most important research component in the medical field which requires machine learning to look over the solution. There are a lot more chances of the occurrence of cancer to a specific person even after the recurrent sessions of the treatment and there is no way to recognize with the clinal knowledge. We need a hand of an expert system to analyze the patients' present condition and need to recognize the better path for the patient for his or her treatment or the life span. Machine learning implementation in the medical domain mostly work on the classification mechanisms in the initial stage of the implementation and we need to work out on implement the ensemble models like the random forest, AdaBoost mechanisms which will give the challenging training methods for the model and the models which are being trained using the ensemble methods will give the accurate results related to any kind of the disease treatments if the concept of implementation of the machine learning is in the initial stage of the implementation. The machine learning models like Random Forest, AdaBoost, SVM, Decision trees gave some respectable results concerning the identification of the patient’s condition who got treatment for cancer and in the post-treatment stage. Among all the models, the random forest gave the highest accuracy of predicting the cancer post-treatment with 93% and SVM got some least among the list with 82% of accuracy in early identification.
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