Cluster Analysis Algorithm in the Analysis of College Students’ Mental Health Education

Zheng, Wanting

doi:10.1155/2022/6394707

“…The method that has been widely used in cancer clustering, the cluster analysis, can be used to study the epidemiological characteristics of cancers, the different types of cancer, and the symptom clusters of patients (25-27). In order to make objects within a class as homogeneous as feasible and those between classes as heterogeneous as possible, the data is divided into various classes in accordance with predetermined criteria (28). For instance, Montazeri et al (25) employed spatial cluster analysis to examine the patterns of occurrence of potentially high-risk breast and prostate cancer clusters in southern Iran.…”

Section: Introductionmentioning

confidence: 99%

Prognostic analysis of breast cancer in Xinjiang based on Cox proportional hazards model and two−step cluster method

Wu

¹

,

Zhao²,

Zhang³

et al. 2023

Front. Oncol.

0

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ObjectiveTo examine the factors that affect the prognosis and survival of breast cancer patients who were diagnosed at the Affiliated Cancer Hospital of Xinjiang Medical University between 2015 and 2021, forecast the overall survival (OS), and assess the clinicopathological traits and risk level of prognosis of patients in various subgroups.MethodFirst, nomogram model was constructed using the Cox proportional hazards models to identify the independent prognostic factors of breast cancer patients. In order to assess the discrimination, calibration, and clinical utility of the model, additional tools such as the receiver operating characteristic (ROC) curve, calibration curve, and clinical decision curve analysis (DCA) were used. Finally, using two-step cluster analysis (TCA), the patients were grouped in accordance with the independent prognostic factors. Kaplan-Meier survival analysis was employed to compare prognostic risk among various subgroups.ResultT-stage, N-stage, M-stage, molecular subtyping, type of operation, and involvement in postoperative chemotherapy were identified as the independent prognostic factors. The nomogram was subsequently constructed and confirmed. The area under the ROC curve used to predict 1-, 3-, 5- and 7-year OS were 0.848, 0.820, 0.813, and 0.791 in the training group and 0.970, 0.898, 0.863, and 0.798 in the validation group, respectively. The calibration curves of both groups were relatively near to the 45° reference line. And the DCA curve further demonstrated that the nomogram has a higher clinical utility. Furthermore, using the TCA, the patients were divided into two subgroups. Additionally, the two groups’ survival curves were substantially different. In particular, in the group with the worse prognosis (the majority of patients did not undergo surgical therapy or postoperative chemotherapy treatment), the T-, N-, and M-stage were more prevalent in the advanced, and the total points were likewise distributed in the high score side.ConclusionFor the survival and prognosis of breast cancer patients in Xinjiang, the nomogram constructed in this paper has a good prediction value, and the clustering results further demonstrated that the selected factors were important. This conclusion can give a scientific basis for tailored treatment and is conducive to the formulation of focused treatment regimens for patients in practical practice.

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“…Study [6] proposes a mental health management model for college students based on wireless sensor networks, which uses wireless sensor systems to complete the statistics of college students' mental health data, thereby achieving automatic retrieval of information. Research [7] proposes a clustering analysis algorithm in the analysis of college students' mental health education, according to which the relevant data of college students' mental health education can be calculated, Provide a foundation for automatic information retrieval.…”

Section: Introductionmentioning

confidence: 99%

Information Retrieval Method of Natural Resources Data based on Hash Algorithm

Li¹

2023

IJACSA

1

0

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In order to improve the ability of searching and identifying information in natural resources data, this paper puts forward a method of searching information in natural resources data based on hash algorithm. Through the data center technology, the problems of information source positioning, data directory organization, data semantic definition and expression, and data entity relationship construction in natural resource data center are solved. Combined with the distribution of resource data stream, the information structure reorganization and data encryption in natural resource data center are realized by using hash algorithm, and the parameters of information quality control model in natural resource data center are established. Through natural resource data governance and semantic reconstruction, the characteristics detection and redundancy arrangement of information data in natural resource data center are realized by standardizing data collection rules, and they are stored in the intermediate database. Through data governance rules, the information in the natural resources data is structured and managed, and stored in the publishing library. Through all kinds of data processing tools, all kinds of data are processed, cleaned and reconstructed, and through Hash algorithm and data aggregation processing, information detection in natural resources data is realized. The simulation results show that the precision rate of natural resource data retrieval by this method is high.

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“…As a mathematical method to solve the system of missing information, the grey clustering algorithm integrates automatic control and operational research methods, which can solve the grey problem in psychological evaluation [ 12 ]. This method has been applied to students' psychological evaluation and achieved ideal application results [ 13 , 14 ]. This study attempts to use the grey clustering algorithm in the psychological assessment of patients after prostatectomy, and then implement more accurate nursing and pelvic floor functional exercises according to the evaluation results.…”

Section: Introductionmentioning

confidence: 99%

Continuous Psychological Nursing Based on Grey Clustering Algorithm in Patients after Transurethral Resection of Prostate

Lu

¹

,

Wu

²

2022

Computational and Mathematical Methods in Medicine

0

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Objective. To explore the effect of continuous psychological nursing based on the grey clustering algorithm on erectile function, bad psychological emotion, and complications in patients after transurethral resection of prostate (TURP). Methods. 98 patients who underwent TURP were randomly divided into observation and control groups (routine nursing). The observation group first used the grey clustering algorithm to evaluate the psychological intelligence, found patients with abnormal psychological behavior, and then implemented continuous psychological nursing combined with pelvic floor muscle exercise. The patients were followed up for 4 months. The International Index of Erectile Function-5 (IIEF-5), the incidence of complications, the Hamilton Depression Scale (HAMD), the Hamilton Anxiety Scale (HAMA) scores, and the nursing satisfaction were analyzed and compared between these two groups. Results. The grey clustering algorithm can accurately reflect the characteristics of patients’ psychological changes. After targeted nursing, compared with the control group, the IIEF-5 in the observation group was higher [( 24.87 ± 1.85 ) vs. ( 22.24 ± 1.47 ), P < 0.05 ], the incidence of total complications was lower (10.20% vs. 26.53%, P < 0.05 ), the score of HAMA was lower [( 6.11 ± 2.57 ) vs. ( 10.98 ± 2.29 ), P < 0.05 ], the score of HAMD was lower [( 6.97 ± 2.85 ) vs. ( 11.35 ± 2.19 ), P < 0.05 ], and the nursing satisfaction was higher (100% vs. 85.71%, P < 0.05 ). Conclusion. Mental intelligence evaluation based on the grey clustering algorithm combined with pelvic floor muscle exercise can significantly improve the rehabilitation effect of erectile function in patients after TURP, reduce the incidence of postoperative complications, and alleviate patients’ anxiety and depression.

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Cluster Analysis Algorithm in the Analysis of College Students’ Mental Health Education

Cited by 7 publications

References 30 publications

Prognostic analysis of breast cancer in Xinjiang based on Cox proportional hazards model and two−step cluster method

Prognostic analysis of breast cancer in Xinjiang based on Cox proportional hazards model and two−step cluster method

Information Retrieval Method of Natural Resources Data based on Hash Algorithm

Continuous Psychological Nursing Based on Grey Clustering Algorithm in Patients after Transurethral Resection of Prostate

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