scholarly journals Adherence predictor variables in AIDS patients: An empirical study using the data mining-based RFM model

2020 ◽  
Author(s):  
Min Li ◽  
Qunwei Wang ◽  
Yinzhong Shen
Keyword(s):  
Data Mining ◽  
Prediction Model ◽  
Clustering Analysis ◽  
Predictor Variable ◽  
Poor Adherence ◽  
Predictor Variables ◽  
Aids Patients ◽  
Decision Algorithm ◽  
Clustering Model ◽  
Rfm Model

Abstract Background Highly active antiretroviral therapy (ART) is still the only effective method to stop the disease progression in acquired immunodeficiency syndrome (AIDS) patients. However, poor adherence to the therapy makes it ineffective. In this work, we construct an adherence prediction model of AIDS patients using the classical recency, frequency and monetary value (RFM) model in the data mining-based customer relationship management model to obtain adherence predictor variables. Methods We cleaned 257305 diagnostic data elements of AIDS outpatients in Shanghai from August 2009 to December 2019 to obtain 16440 elements. We tested the RFM and RFm (R: recent consultation month, F: consultation frequency, M/m: total/average medical costs per visit) models, three clustering methods (K-means, Kohonen and two-step clustering) and four decision algorithms (C5.0, the classification and regression tree, Chi-square Automatic Interaction Detector and Quick, Unbiased, Efficient, Statistical Tree) to select the optimal combination. The optimal model and clustering analysis were used to divide the patients into two groups (good and poor adherence), then the optimal decision algorithm was used to construct the prediction model of adherence and obtain its predictor variables. Results The results revealed that the RFm model, K-means clustering analysis and C5.0 algorithm were optimal. After three rounds of k-means clustering analysis, the optimal RFm clustering model quality was 0.8, 10614 elements were obtained, including 9803 and 811 from patients with good or poor adherence, respectively, and five types of patients were identified. The prediction model had an accuracy of 100% with the recent consultation month as an important adherence predictor variable. Conclusions This work presented a prediction model for medication adherence in AIDS patients at the designated AIDS center in Shanghai, using the RFm model and the k-means and C5.0 algorithms. The model can be expanded to include patients from other centers in China and worldwide.

scholarly journals Adherence predictor variables in AIDS patients: an empirical study using the data mining-based RFM model

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Min Li ◽  
Qunwei Wang ◽  
Yinzhong Shen
Keyword(s):  
Data Mining ◽  
Prediction Model ◽  
Clustering Analysis ◽  
Predictor Variable ◽  
Poor Adherence ◽  
Predictor Variables ◽  
Aids Patients ◽  
Decision Algorithm ◽  
Clustering Model ◽  
Rfm Model

Abstract Background Highly active antiretroviral therapy (ART) is still the only effective method to stop the disease progression in acquired immunodeficiency syndrome (AIDS) patients. However, poor adherence to the therapy makes it ineffective. In this work, we construct an adherence prediction model of AIDS patients using the classical recency, frequency and monetary value (RFM) model in the data mining-based customer relationship management model to obtain adherence predictor variables. Methods We cleaned 257,305 diagnostic data elements of AIDS outpatients in Shanghai from August 2009 to December 2019 to obtain 16,440 elements. We tested the RFM and RFm (R: recent consultation month, F: consultation frequency, M/m: total/average medical costs per visit) models, three clustering methods (K-means, Kohonen and two-step clustering) and four decision algorithms (C5.0, the classification and regression tree, Chi-square Automatic Interaction Detector and Quick, Unbiased, Efficient, Statistical Tree) to select the optimal combination. The optimal model and clustering analysis were used to divide the patients into two groups (good and poor adherence), then the optimal decision algorithm was used to construct the prediction model of adherence and obtain its predictor variables. Results The results revealed that the RFm model, K-means clustering analysis and C5.0 algorithm were optimal. After three rounds of k-means clustering analysis, the optimal RFm clustering model quality was 0.8, 10,614 elements were obtained, including 9803 and 811 from patients with good or poor adherence, respectively, and five types of patients were identified. The prediction model had an accuracy of 100% with the recent consultation month as an important adherence predictor variable. Conclusions This work presented a prediction model for medication adherence in AIDS patients at the designated AIDS center in Shanghai, using the RFm model and the k-means and C5.0 algorithms. The model can be expanded to include patients from other centers in China and worldwide.

scholarly journals Adherence predictor variables in AIDS patients: An empirical study using the data mining-based RFM model

2020 ◽  
Author(s):  
Min Li ◽  
Qunwei Wang ◽  
Yinzhong Shen
Keyword(s):  
Data Mining ◽  
Prediction Model ◽  
Clustering Analysis ◽  
Predictor Variable ◽  
Poor Adherence ◽  
Predictor Variables ◽  
Aids Patients ◽  
Decision Algorithm ◽  
Clustering Model ◽  
Rfm Model

Abstract Background: Highly active antiretroviral therapy (ART) is still the only effective method to stop the disease progression in acquired immunodeficiency syndrome (AIDS) patients. However, poor adherence to the therapy makes it ineffective. In this work, we construct an adherence prediction model of AIDS patients using the classical recency, frequency and monetary value (RFM) model in the data mining-based customer relationship management model to obtain adherence predictor variables.Methods: We cleaned 257305 diagnostic data elements of AIDS outpatients in Shanghai from August 2009 to December 2019 to obtain 16440 elements. We tested the RFM and RFm (R: recent consultation month, F: consultation frequency, M/m: total/average medical costs per visit) models, three clustering methods (K-means, Kohonen and two-step clustering) and four decision algorithms (C5.0, the classification and regression tree, Chi-square Automatic Interaction Detector and Quick, Unbiased, Efficient, Statistical Tree) to select the optimal combination. The optimal model and clustering analysis were used to divide the patients into two groups (good and poor adherence), then the optimal decision algorithm was used to construct the prediction model of adherence and obtain its predictor variables.Results: The results revealed that the RFm model, K-means clustering analysis and C5.0 algorithm were optimal. After three rounds of k-means clustering analysis, the optimal RFm clustering model quality was 0.8, 10614 elements were obtained, including 9803 and 811 from patients with good or poor adherence, respectively, and five types of patients were identified. The prediction model had an accuracy of 100% with the recent consultation month as an important adherence predictor variable.Conclusions: This work presented a prediction model for medication adherence in AIDS patients at the designated AIDS center in Shanghai, using the RFm model and the k-means and C5.0 algorithms.

Data Mining Techniques to Enhance Customer Lifetime Value

2011 ◽  
Vol 225-226 ◽  
pp. 3-7
Author(s):  
Chia Chia Lin ◽  
Dong Her Shih
Keyword(s):  
Data Mining ◽  
Clustering Analysis ◽  
Critical Factor ◽  
Department Store ◽  
Customer Lifetime Value ◽  
Data Mining Techniques ◽  
Customer Lifetime ◽  
Rfm Model ◽  
Lifetime Value

It is proved by many studies that it is more costly to acquire than to retain customers. Consequently, evaluating current customers to keep high value customers and enhance their lifetime value becomes a critical factor to decide the success or failure of a business. This study applies data from customer and transaction databases of a department store, based on RFM model to do clustering analysis to recognize high value customer groups for cross-selling promotions.

scholarly journals Data Analysis of College Students’ Mental Health Based on Clustering Analysis Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yichen Chu ◽  
Xiaojian Yin
Keyword(s):  
Mental Health ◽  
College Students ◽  
Data Mining ◽  
Clustering Analysis ◽  
Management System ◽  
Clustering Algorithm ◽  
Analysis Algorithm ◽  
Advantages And Disadvantages ◽  
Clustering Model ◽  
Psychological Management

Mental health is an important basic condition for college students to become adults. Educators gradually attach importance to strengthening the mental health education of college students. This paper makes a detailed analysis and research on college students’ mental health, expounds the development and application of clustering analysis algorithm, applies the distance formula and clustering criterion function commonly used in clustering analysis, and makes a specific description of some classic algorithms of clustering analysis. Based on expounding the advantages and disadvantages of fast-clustering analysis algorithm and hierarchical clustering analysis algorithm, this paper introduces the concept of the two-step clustering algorithm, discusses the algorithm flow of clustering model in detail, and gives the algorithm flow chart. The main work of this paper is to analyze the clustering algorithm of students’ mental health database formed by mental health assessment tool test, establish a data mining model, mine the database, analyze the state characteristics of different college students’ mental health, and provide corresponding solutions. In order to meet the needs of the psychological management system based on the clustering analysis method, the clustering analysis algorithm is used to cluster the data. Based on the original database, this paper establishes the methods of selecting, cleaning, and transforming the data of students’ psychological archives. Finally, it expounds on the application of data mining in students’ psychological management system and summarizes and prospects the implementation of the system.

scholarly journals Chiller Optimization Using Data Mining Based on Prediction Model, Clustering and Association Rule Mining

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6494
Author(s):  
Elsa Chaerun Nisa ◽  
Yean-Der Kuan ◽  
Chin-Chang Lai
Keyword(s):  
Data Mining ◽  
Prediction Model ◽  
Association Rules ◽  
Clustering Analysis ◽  
High Performance ◽  
Technology Development ◽  
Meteorological Data ◽  
Strong Association ◽  
Operational Parameters ◽  
Data Mining Technique

The chiller is the major energy consuming HVAC component in a building. Currently, huge chiller data is easy to obtain due to Internet of Things (IoT) technology development. In order to optimize the chiller system, this study presents a data mining technique that utilizes the available chiller data. The data mining techniques used are prediction model, clustering analysis, and association rules mining (ARM) analysis. The dataset was collected every minute for a year from a water-cooled chiller at an institutional building in Taiwan and from meteorological data. The power consumption prediction model was built using deep neural networks with 0.955 of R2, 4.470 of MAE, and 6.716 of RMSE. Clustering analysis was performed using the k-means algorithm and ARM analysis was performed using Apriori algorithm. Each cluster identifies those operational parameters that have strong association rules with high performance. The operational parameters from ARM were simulated using the prediction model. The simulation result shows that the ARM operational parameters can successfully save the energy consumption by 22.36 MWh or 18.17% in a year.

scholarly journals The Effect of Dual Language Activation on L2-Induced Changes in L1 Speech within a Code-Switched Paradigm

Languages ◽  
2021 ◽  
Vol 6 (3) ◽  
pp. 114
Author(s):  
Ulrich Reubold ◽  
Sanne Ditewig ◽  
Robert Mayr ◽  
Ineke Mennen
Keyword(s):  
Dual Language ◽  
Native Speakers ◽  
Predictor Variable ◽  
Predictor Variables ◽  
Acoustic Measures ◽  
Before And After ◽  
Language Activation ◽  
Dual Activation ◽  
Induced Changes

The present study sought to examine the effect of dual language activation on L1 speech in late English–Austrian German sequential bilinguals, and to identify relevant predictor variables. To this end, we compared the English speech patterns of adult migrants to Austria in a code-switched and monolingual condition alongside those of monolingual native speakers in England in a monolingual condition. In the code-switched materials, German words containing target segments known to trigger cross-linguistic interaction in the two languages (i.e., [v–w], [ʃt(ʁ)-st(ɹ)] and [l-ɫ]) were inserted into an English frame; monolingual materials comprised English words with the same segments. To examine whether the position of the German item affects L1 speech, the segments occurred either before the switch (“He wants a Wienerschnitzel”) or after (“I like Würstel with mustard”). Critical acoustic measures of these segments revealed no differences between the groups in the monolingual condition, but significant L2-induced shifts in the bilinguals’ L1 speech production in the code-switched condition for some sounds. These were found to occur both before and after a code-switch, and exhibited a fair amount of individual variation. Only the amount of L2 use was found to be a significant predictor variable for shift size in code-switched compared with monolingual utterances, and only for [w]. These results have important implications for the role of dual activation in the speech of late sequential bilinguals.

RELATIONS OF MOTOR ABILITY OF BALANCE AND SUCCESS OF PERFORMANCE OF GYMNASTICS ELEMENTS ON THE FLOOR EXERCISE

sportlogia ◽  
2021 ◽  
Vol 17 (1) ◽  
pp. 34-43
Author(s):  
Saša Jovanović ◽  
◽  
Snežana Bijelić ◽  
Adriana Ljubojević ◽  
Dalibor Fulurija ◽  
...  
Keyword(s):  
Physical Education ◽  
Prediction Model ◽  
Motor Skills ◽  
Predictive Value ◽  
Predictor Variable ◽  
The Other ◽  
Motor Ability ◽  
Training Methodology ◽  
Balance Ability ◽  
The Relationship

The purpose of this study is to investigate the relationship between motor ability for balance and the performance of selected gymnastic elements on the floor in students aged 7-8 years, to provide an overview of the current motor status of the respondents at this age, and to develop suggestions for possible changes in the curriculum at this age, and to develop suggestions for supplementing training methodology. Training of selected gymnastics elements was conducted on a sample of 42 subjects who had no previous experience in performing gymnastics elements during regular physical education classes, and the predictor variable was tested using four tests assessing motor balance ability. The tests assessing motor balance ability showed a statistically significant predictive value for the performance of all three gymnastics exercises. It is noticeable that the value of the prediction model increased the more complex an item was derived, indicating the complexity of the motor balance space and the high and stable level of the same in the subjects at the time of testing. Regarding the tests used, it can be noted that the test FLAM was significantly involved in the prediction of performance success in all three gymnastic elements, while the other two tests showed their predictive value in the execution of the hand stand. On the other hand, the study shows that the gymnastic elements used should be used in physical education classes to contribute to the promotion and development of all motor skills of students and as part of the preparation for the execution of more complex elements on the floor and apparatus in higher grades.

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