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

• Published in: Entropy (Basel, Switzerland) Vol. 24; no. 2; p. 253
• Main Authors: Song, Chaohong, Li, Xinran
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 08.02.2022; MDPI
• Subjects: Algorithms; approximate entropy; Blood; cancer prediction; Cost analysis; cost-sensitive learning; Endometrial cancer; Entropy; Health services; imbalanced dataset; Kinases; KNN; Markers; Medical research; Model accuracy; Ovarian cancer; Proteins; sample entropy; Serum proteins; cancer prediction; KNN; imbalanced dataset; approximate entropy; cost-sensitive learning; sample entropy
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e24020253

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.