Siddalingappa, Rashmi ORCID: https://orcid.org/0000-0001-9786-8436 and Kanagaraj, Sekar (2023) K-nearest-neighbor algorithm to predict the survival time and classification of various stages of oral cancer: a machine learning approach. F1000 Research.

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Abstract

Background:For years now, cancer treatments have entailed tried-and-true methods. Yet, oncologists and clinicians recommend a series of surgeries, chemotherapy, and radiation therapy. Yet, even amidst these treatments, the number of deaths due to cancer increases at an alarming rate. The prognosis of cancer patients is influenced by mutations, age, and various cancer stages. However, the association between these variables is unclear.

Methods: The present work adopts a machine learning technique—k-nearest neighbor; for both regression and classification tasks, regression for predicting the survival time of oral cancer patients, and classification for classifying the patients into one of the predefined oral cancer stages. Two cross-validation approaches—hold-out and k-fold methods—have been used to examine the prediction results.

Results: The experimental results show that the k-fold method performs better than the hold-out method, providing the least mean absolute error score of 0.015. Additionally, the model classifies patients into a valid group. Of the 429 records, 97 (out of 106), 99 (out of 119), 95 (out of 113), and 77 (out of 91) were classified to its correct label as stages – 1, 2, 3, and 4. The accuracy, recall, precision, and F-measure for each classification group obtained are 0.84, 0.85, 0.85, and 0.84.

Conclusions: The study showed that aged patients with a higher number of mutations than young patients have a higher risk of short survival. Senior patients with a more significant number of mutations have an increased risk of getting into the last cancer stage

Item Type:	Article
Status:	Published
DOI:	10.12688/f1000research.75469.1
Subjects:	Q Science > Q Science (General) Q Science > Q Science (General) > Q325 Machine learning
School/Department:	London Campus
URI:	https://ray.yorksj.ac.uk/id/eprint/12878

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