A data mining approach for modeling churn behavior via RFM model in specialized clinics Case study: A public sector hospital in Tehran

Mohammadzadeh, Mehdi; Hoseini, Zeinab Zare; Derafshi, Hamid

doi:10.1016/j.procs.2017.11.206

Cited by 26 publications

(31 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Three clustering methods (Kmeans, Kohonen and two-step clustering) were used to construct the clustering models, in which four decision algorithms (C5.0, classi cation and regression tree (CART), Chi-square Automatic Interaction Detector (CHAID) and Quick, Unbiased, E cient, Statistical Tree (QUEST)) were used in each model to construct several preliminary prediction models. From these models, we determined the optimal RFM(m) model [25][26]29,[31][32][33], clustering analysis method [25,[31][32][33] and decision algorithm based on the quality of the model [25,[31][32][33] and stability of important predictor variables, which were used for the adherence prediction model experiment. The models in this study were calculated and built according to the SPSS Modeler 18.0 software package.…”

Section: Variable Generation and Descriptive Statistical Analysismentioning

confidence: 99%

“…In this experiment [25][26]29,[31][32][33][34], we used the optimal RFM(m) model and methods found in the previous experiment to construct the best clustering model, separate patients with good adherence from those with poor adherence and identify important variables for adherence prediction. The literature methods were used as references, and the optimal decision algorithm was employed, with good and poor adherence as targets.…”

Section: Validating the Adherence Prediction Model And Obtaining The Variablesmentioning

confidence: 99%

See 1 more Smart Citation

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

Li¹,

Wang²,

Shen³

2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Variable Generation and Descriptive Statistical Analysismentioning

confidence: 99%

Section: Validating the Adherence Prediction Model And Obtaining The Variablesmentioning

confidence: 99%

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

Li¹,

Wang²,

Shen³

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Besides, Hosseini and Mohammadzadeh (2016) applied LRFM model to analyze patients from emergency clinics for customer value analysis based on patients' characteristics, preferences, and activities. Furthermore, Mohammadzadeh et al (2017) employed LRFM model to calculate each patient's lifetime value such that the health care provider can classify patients into four different types of patients. In doing so, health care providers could further implement customer relationship management (CRM) to strengthen the relationship with patients and apply different marketing strategies to enhance their profits and reduce costs due to patients' loss.…”

Section: Introductionmentioning

confidence: 99%

An LRFM Model to Analyze Outpatient Loyalty From a Medical Center in Taiwan

Chao

Chen

2021

SAGE Open

View full text Add to dashboard Cite

This research is intended to study the behaviors of outpatients in a medical center and constructs a set of data exploration procedures such that hospital management can deal with patient relationship management more effectively. This study adopts LRFM (length, recency, frequency, and monetary) model and cluster analysis, including self-organizing maps and K-means method, to categorize 321,908 outpatients of the medical center into 12 groups and then uses the multidimensional customer clustering philosophy to classify the outpatients. Outpatients can be categorized into five different types of groups, namely, core customer groups, potential customer groups, new customer groups, lost customer groups, and resource-consuming customer groups. In addition, seven types of outpatients based on five types of categories are identified. The similarities and differences of each group based on the patients’ characteristics are analyzed to give differentiation strategy advices for hospital management. Hospital management thus can design the optimal service strategies, provide the best care services, enhance hospital’s performance, and reduce the overall cost to establish quality relationships with outpatients.

show abstract

“…Identification of the most profit-generating customers and segmentation of customers are quite vital [3]. Previous studies reveal that recency, frequency, and monetary (RFM) analysis and frequent pattern mining can be successfully used or integrated to discover valuable patterns of customer purchase behavior [3][4][5][6][7][8]. Dursun and Caber [3] took the RFM analysis for profiling profitable hotel customers and related customers were divided into eight groups.…”

Section: Introductionmentioning

confidence: 99%

“…Coussement et al [6] employed RFM analysis, logistic regression, and decision trees for the customers' segmentation and identification. Mohammadzadeh et al [7] employed k-means clustering for identifying target patient customers and then conducted the prediction of customers churn behavior via the RFM model based on the decision tree classifier. Song et al [8] employed RFM considering parameters with time series to cluster customers and identify target customers.…”

Section: Introductionmentioning

confidence: 99%

A Modified Bayesian Network Model to Predict Reorder Level of Printed Circuit Board

Kim

Jin

et al. 2018

Applied Sciences

View full text Add to dashboard Cite

Featured Application: The research was motivated by the requirement of a printed circuit board (PCB) manufacturer and the application of the work is to identify the repeated PCB orders for batch production according to the predicted reorder level.Abstract: Identifying the printed circuit board (PCB) orders with high reorder frequency for batch production can facilitate production capacity balance and reduce cost. In this paper, the repeated orders identification problem is transformed to a reorder level prediction problem. A prediction model based on a modified Bayesian network (BN) with Monte Carlo simulations is presented to identify related variables and evaluate their effects on the reorder level. From the historically accumulated data, different characteristic variables are extracted and specified for the model. Normalization and principal component analysis (PCA) are employed to reduce differences and the redundancy of the datasets, respectively. Entropy minimization based binning is presented to discretize model variables and, therefore, reduce input type and capture better prediction performance. Subsequently, conditional mutual information and link strength percentage are combined for the establishment of BN structure to avoid the defect of tree augmented naïve BN that easily misses strong links between nodes and generates redundant weak links. Monte Carlo simulation is conducted to weaken the influence of uncertainty factors. The model's performance is compared to three advanced approaches by using the data from a PCB manufacturer and results demonstrate that the proposed method has high prediction accuracy.

show abstract

A data mining approach for modeling churn behavior via RFM model in specialized clinics Case study: A public sector hospital in Tehran

Cited by 26 publications

References 8 publications

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

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

An LRFM Model to Analyze Outpatient Loyalty From a Medical Center in Taiwan

A Modified Bayesian Network Model to Predict Reorder Level of Printed Circuit Board

Contact Info

Product

Resources

About