Improved RFM Model for Customer Segmentation Using Hybrid Meta-heuristic Algorithm in Medical IoT Applications

Abstract

In Internet of Things (IoT) business applications, healthcare records and medical web service management have emerging technologies tremendous amount of data in daily transactions. The cloud service providers have attracted the attention of a huge number of user requests based on different Quality of Service (QoS) factors. The data gained researchers attention to predict user behavior through the production of IoT applications. Artificial Intelligence (AI)-based techniques have a great impact on analyzing and detection of web service segmentation and user behavior in selecting and allocating online services and online stores with wireless communications and smart devices. This research improves the Redundancy, Frequency, and Maintenance value (RFM) model to evaluate healthcare records using a hybrid Grouping Genetic Algorithm (GGA) and Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithms. In this hybrid method, using the GGA algorithm, the features selection is applied to find the optimal features for healthcare records of customer segmentation. After that, by applying the RFM model on the data output of the genetic algorithm, the data are clustered. Finally, by applying the DBSCAN algorithm, the most suitable case for clustering will be selected. The simulation results show that the accuracy of the genetic algorithm is 97% and the final clustering accuracy is 92%.