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9 Ways to Apply Predictive Analytics to Healthcare | Icosystem

9 Ways to Apply Predictive Analytics to Healthcare | Icosystem | Big data analysis workflows | Scoop.it
Here are nine ways using predicitve analytics can improve results for both the patient and businesses participating in the complex healthcare market: 1.
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DatumBox API Equips Third Party Apps with Machine Learning Capabilities

DatumBox API Equips Third Party Apps with Machine Learning Capabilities | Big data analysis workflows | Scoop.it
DatumBox provides a machine learning platform that specializes in natural language processing. The DatumBox API enables developers to integrate various DatumBox tools into third party applications, websites, or software.

Via Richard Kastelein
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Prediction of Subscriber Churn Using Social Network Analysis - Phadke - 2013 - Bell Labs Technical Journal - Wiley Online Library

Prediction of Subscriber Churn Using Social Network Analysis - Phadke - 2013 - Bell Labs Technical Journal - Wiley Online Library | Big data analysis workflows | Scoop.it

In today's world, mobile phone penetration has reached a saturation point. As a result, subscriber churn has become an important issue for mobile operators as subscribers switch operators for a variety of reasons. Mobile operators typically employ churn prediction algorithms based on service usage metrics, network performance indicators, and traditional demographic information. A newly emerging technique is the use of social network analysis (SNA) to identify potential churners. Intuitively, a subscriber who is churning will have an impact on the churn propensity of his social circle. Call detail records are useful to understand the social connectivity of subscribers through call graphs but do not directly provide the strength of their relationship or have enough information to determine the diffusion of churn influence. In this paper, we present a way to address these challenges by developing a new churn prediction algorithm based on a social network analysis of the call graph. We provide a formulation that quantifies the strength of social ties between users based on multiple attributes and then apply an influence diffusion model over the call graph to determine the net accumulated influence from churners. We combine this influence and other social factors with more traditional metrics and apply machine-learning methods to compute the propensity to churn for individual users. We evaluate the performance of our algorithm over a real data set and quantify the benefit of using SNA in churn prediction. © 2013 Alcatel-Lucent.

 

Source: http://onlinelibrary.wiley.com/doi/10.1002/bltj.21575/full

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