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Cost-Sensitive KNN Algorithm for Cancer Prediction Based on Entropy Analysis

Early diagnosis of cancer is beneficial in the formulation of the best treatment plan; it can improve the survival rate and the quality of patient life. However, imaging detection and needle biopsy usually used not only find it difficult to effectively diagnose tumors at early stage, but also do gre...

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Autores principales: Song, Chaohong, Li, Xinran
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871087/
https://www.ncbi.nlm.nih.gov/pubmed/35205547
http://dx.doi.org/10.3390/e24020253
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author Song, Chaohong
Li, Xinran
author_facet Song, Chaohong
Li, Xinran
author_sort Song, Chaohong
collection PubMed
description Early diagnosis of cancer is beneficial in the formulation of the best treatment plan; it can improve the survival rate and the quality of patient life. However, imaging detection and needle biopsy usually used not only find it difficult to effectively diagnose tumors at early stage, but also do great harm to the human body. Since the changes in a patient’s health status will cause changes in blood protein indexes, if cancer can be diagnosed by the changes in blood indexes in the early stage of cancer, it can not only conveniently track and detect the treatment process of cancer, but can also reduce the pain of patients and reduce the costs. In this paper, 39 serum protein markers were taken as research objects. The difference of the entropies of serum protein marker sequences in different types of patients was analyzed, and based on this, a cost-sensitive analysis model was established for the purpose of improving the accuracy of cancer recognition. The results showed that there were significant differences in entropy of different cancer patients, and the complexity of serum protein markers in normal people was higher than that in cancer patients. Although the dataset was rather imbalanced, containing 897 instances, including 799 normal instances, 44 liver cancer instances, and 54 ovarian cancer instances, the accuracy of our model still reached 95.21%. Other evaluation indicators were also stable and satisfactory; precision, recall, F1 and AUC reach 0.807, 0.833, 0.819 and 0.92, respectively. This study has certain theoretical and practical significance for cancer prediction and clinical application and can also provide a research basis for the intelligent medical treatment.
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spelling pubmed-88710872022-02-25 Cost-Sensitive KNN Algorithm for Cancer Prediction Based on Entropy Analysis Song, Chaohong Li, Xinran Entropy (Basel) Article Early diagnosis of cancer is beneficial in the formulation of the best treatment plan; it can improve the survival rate and the quality of patient life. However, imaging detection and needle biopsy usually used not only find it difficult to effectively diagnose tumors at early stage, but also do great harm to the human body. Since the changes in a patient’s health status will cause changes in blood protein indexes, if cancer can be diagnosed by the changes in blood indexes in the early stage of cancer, it can not only conveniently track and detect the treatment process of cancer, but can also reduce the pain of patients and reduce the costs. In this paper, 39 serum protein markers were taken as research objects. The difference of the entropies of serum protein marker sequences in different types of patients was analyzed, and based on this, a cost-sensitive analysis model was established for the purpose of improving the accuracy of cancer recognition. The results showed that there were significant differences in entropy of different cancer patients, and the complexity of serum protein markers in normal people was higher than that in cancer patients. Although the dataset was rather imbalanced, containing 897 instances, including 799 normal instances, 44 liver cancer instances, and 54 ovarian cancer instances, the accuracy of our model still reached 95.21%. Other evaluation indicators were also stable and satisfactory; precision, recall, F1 and AUC reach 0.807, 0.833, 0.819 and 0.92, respectively. This study has certain theoretical and practical significance for cancer prediction and clinical application and can also provide a research basis for the intelligent medical treatment. MDPI 2022-02-08 /pmc/articles/PMC8871087/ /pubmed/35205547 http://dx.doi.org/10.3390/e24020253 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Song, Chaohong
Li, Xinran
Cost-Sensitive KNN Algorithm for Cancer Prediction Based on Entropy Analysis
title Cost-Sensitive KNN Algorithm for Cancer Prediction Based on Entropy Analysis
title_full Cost-Sensitive KNN Algorithm for Cancer Prediction Based on Entropy Analysis
title_fullStr Cost-Sensitive KNN Algorithm for Cancer Prediction Based on Entropy Analysis
title_full_unstemmed Cost-Sensitive KNN Algorithm for Cancer Prediction Based on Entropy Analysis
title_short Cost-Sensitive KNN Algorithm for Cancer Prediction Based on Entropy Analysis
title_sort cost-sensitive knn algorithm for cancer prediction based on entropy analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871087/
https://www.ncbi.nlm.nih.gov/pubmed/35205547
http://dx.doi.org/10.3390/e24020253
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