Heart disease is a common disease affecting human health. Electrocardiogram (ECG) classification is the most effective and direct method to detect heart disease, which is helpful to the diagnosis of most heart disease symptoms. At present, most ECG diagnosis depends on the personal judgment of medical staff, which leads to heavy burden and low efficiency of medical staff. Automatic ECG analysis technology will help the work of relevant medical staff. In this paper, we use the MIT-BIH ECG database to extract the QRS features of ECG signals by using the Pan-Tompkins algorithm. After extraction of the samples, -means clustering is used to screen the samples, and then, RBF neural network is used to analyze the ECG information. The classifier trains the electrical signal features, and the classification accuracy of the final classification model can reach 98.9%. Our experiments show that this method can effectively detect the abnormality of ECG signal and implement it for the diagnosis of heart disease.

Study Design.: Genetic cross-over study of intervertebral disc degeneration (IDD) and its associated risk factors. Objective.: The purpose is to identify molecular biomarkers that may be involved in the IDD process and to provide effective recommendations in combination with drug analysis via systems biology methods. Summary of Background Data.: The pathogenesis and genetic links of IDD are still unclear. Related research is mainly based on a single data set or gene, and the impact of related risk factors on IDD is often ignored. Methods.: Identifying disease-associated biomarkers and therapeutic targets through a systems biology approach that Integrative network-based gene and multi-omics analysis. In this study, the differential expression of genes was analyzed through NCBI datasets, followed by enrichment analysis. The central protein was identified through the establishment of protein-protein interaction (PPI) network. Key genes were screened out by VENN diagram and reasonable suggestions were put forward based on gene-chemical drug analysis. Results.: The IDD database analysis revealed 669 differentially expressed genes (DEGs) which were 22, 26, 168, 5, 38, 36, and 16 common DEGs with AG, SM, DEP, NAD, CED, OB, and HFD, respectively. GO and KEGG enrichment analysis may reveal the pathway by which these DEGs were involved. PPI network identified 10 central proteins including CCNB1, RETN, HMMR, BUB1, MPO, OIP5, HP, KIF11, BUB1B, and CDC25A. Three key genes BUB1, BUB1B, and CCNB1 were screened out and their expression might be related to the pathogenesis of IDD. According to the three chemical Dexamethasone, Nicotine, and Resvera-trol obtained from the analysis of genes-chemical drugs, reasonable treatment suggestions were put forward. Conclusion.: Genetic association between IDD and risk factors in the general population was revealed by association network. Important gene-related molecular pathways and chemical drugs closely related to IDD have been found. Further study can provide guidance for the treatment and prognosis of IDD. Level of Evidence: N/A

A critical step for gene selection algorithms using rough set theory is the establishment of a gene evaluation function to assess the classification ability of candidate gene subsets. The concept of dependency in a classic neighborhood rough set model plays the role of this evaluation function. This criterion only notes the information provided by the lower approximation and omits the upper approximation, which may result in the loss of some important information. This paper proposes gene selection algorithms within a single-cell gene decision space by employing self-information, taking into account both lower and upper approximations. Initially, the distance between gene expression values within each subspace is defined to establish the tolerance relation on the cell set. Subsequently, self-information is introduced through the lens of tolerance classes. The relationship between these measures and their respective properties is then examined in detail. For gene expression data, the proposed self-information metric demonstrates superiority over other measures by accounting for both lower and upper approximations, thereby facilitating the selection of optimal gene subsets. Finally, gene selection algorithms within a single-cell gene decision space are developed based on the proposed self-information metric, and experiments conducted on 10 publicly available single-cell datasets indicate that the classification performance of the proposed algorithms can be enhanced through the selection of genes pertinent to classification. The results demonstrate that $Fi-SI$ achieves an average classification accuracy of 93.7% (KNN) while selecting 48.3% fewer genes than Fisher’s score.

Rough set methodology has gained broad acceptance as a potent mathematical apparatus for feature selection within data mining and machine learning. Yet, classical rough sets hinge on equivalence relations to partition the universe, thereby demanding strict reflexivity, symmetry, and transitivity conditions that are arduous to satisfy in realistic settings. Although fuzzy rough sets have been explored to mitigate this rigidity, the entropy-based uncertainty measures employed in fuzzy approximation spaces remain acutely sensitive to data quality and noise corruption, potentially inducing severe bias in feature evaluation. Moreover, the literature currently lacks noise-tolerant uncertainty measures capable of accommodating a controlled fraction of classification errors while safeguarding the discriminative strength of feature subsets. Inspired by these gaps, this study develops a feature selection framework grounded in variable precision fuzzy entropy within the fuzzy rough set context. To this end, fuzzy decision is adopted to portray the membership degree of samples relative to decision classes, thereby enabling more precise detection and elimination of redundant attributes during approximation. An uncertainty quantifier termed fuzzy relational entropy is then introduced to appraise the distinguishing power of fuzzy similarity relations generated by attribute subsets. Leveraging fuzzy decision, a portfolio of uncertainty measure variants, specifically the variable precision joint discriminant index, the variable precision conditional discriminant index, and the variable precision mutual discriminant index, is developed to counteract noisy data effects. These variable precision discriminant indexes sanction a regulated error proportion and afford a measure of noise resistance. Finally, knowledge reduction for fuzzy decision systems is attacked from the angle of discriminative capability preservation, and a heuristic feature selection algorithm is crafted around the variable precision conditional discriminant index. Evaluation on twelve public UCI datasets reveals that the proposed algorithm effectively prunes redundant features and delivers competitive results against three representative alternatives: classical rough set, neighbourhood-based discriminant index, and fuzzy rough set feature selection. Additionally, it sustains stable classification performance across an extensive sweep of the variable precision parameter.