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BACKGROUND: Hepatitis B Virus (HBV) reactivation is the most common complication for patients with primary liver cancer (PLC) after radiotherapy. How to reduce the reactivation of HBV has been a hot topic in the study of postoperative radiotherapy for liver cancer. OBJECTIVE: To find out the inducement of HBV reactivation, a feature selection algorithm (MIC-CS) using maximum information coefficient (MIC) combined with cosine similarity (CS) was proposed to screen the risk factors that may affect HBV reactivation. METHOD: Firstly, different factors were coded and MIC between patients was calculated to acquire the association between different factors and HBV reactivation. Secondly, a cosine similarity algorithm was constructed to calculate the similarity relationship between different factors, thus removing redundant information. Finally, combined with the weight of the two, the potential risk factors were sorted and the key factors leading to HBV reactivation were selected. RESULTS: The results indicated that HBV baseline, external boundary, TNM, KPS score, VD, AFP, and Child-Pugh could lead to HBV reactivation after radiotherapy. The classification model was constructed for the above factors, with the highest classification accuracy of 84% and the AUC value of 0.71. CONCLUSION: Comparing multiple feature selection methods, the results showed that the effect of the MIC-CS was significantly better than MIM, CMIM, and mRMR, so it has a very broad application prospect. [ABSTRACT FROM AUTHOR]

Data classification is the most common task in machine learning, and feature selection is the key step in the classification task. Common feature selection methods mainly analyze the maximum correlation and minimum redundancy between feature factors and tags while ignoring the impact of the number of key features, which will inevitably lead to waste in subsequent classification training. To solve this problem, a feature selection algorithm (SSMI) based on the combination of sinusoidal sequences and mutual information is proposed. First, the mutual information between each feature and tag is calculated, and the interference information in high-dimensional data is removed according to the mutual information value. Second, a sine function is constructed, and sine ordering is carried out according to the mutual information value and feature mean value between different categories of the same feature. By adjusting the period and phase value of the sequence, the feature set with the largest difference is found, and the subset of key features is obtained. Finally, three machine learning classifiers (KNN, RF, SVM) are used to classify key feature subsets, and several feature selection algorithms (JMI, mRMR, CMIM, SFS, etc.) are compared to verify the advantages and disadvantages of different algorithms. Compared with other feature selection methods, the SSMI algorithm obtains the least number of key features, with an average reduction of 15 features. The average classification accuracy has been improved by 3% on the KNN classifier. On the HBV and SDHR datasets, the SSMI algorithm achieved classification accuracy of 81.26% and 83.12%, with sensitivity and specificity results of 76.28%, 87.39% and 68.14%, 86.11%, respectively. This shows that the SSMI algorithm can achieve higher classification accuracy with a smaller feature subset. [ABSTRACT FROM AUTHOR]

[Display omitted] • A cosine transform-based similarity calculation method is proposed to cope with information loss in data discretization when the strategy of mutual information is adopted for feature selection. Feature selection (FS) based on mutual information (MI) metrics needs to discretize the data in preprocessing, which is a convenient way to identify correlation between features. However, information loss often occurs in data discretization. In order to solve this information loss problem, this paper proposes a FS algorithm based on cosine similarity coefficient and information measurement criterion (CSCIM_FS). First, the MI between features and tags is calculated, and features are sorted out according to the MI calculated. Then, a feature matrix is constructed to transform the one-dimensional feature sequence into a two-dimensional square matrix. Next, cosine transform is adopted to obtain the high-frequency components of the feature matrix, and sampling is conducted to derive the hash fingerprint of the feature matrix. After that, the similarity between every two features is calculated on the basis of the hash fingerprints of different features. Finally, the feature weight is calculated according to tags, the MI and similarity between features, and a key feature subset is obtained and used to conduct feature selection from the data. The experimental results on several UCI public datasets show that CSCIM_FS algorithm selected a feature subset with high accuracy, and that this algorithm performs better than MIM, CMIM, mRMR and other algorithms. [ABSTRACT FROM AUTHOR]