2019 LACCEI - Montego Bay, Jamaica

Permanent URI for this collectionhttps://axces.info/handle/10.18687/47

"Industry, Innovation, and Infrastructure for Sustainable Cities and Communities". Montego Bay, Jamaica. July 24 - 26, 2019

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Evaluation of operational process variables in healthcare using process mining and data visualization techniques
(LACCEI, Inc., 2019-07) Armas, Jimmy; Coronado Torres, André; Evangelista Pescoran, Misael; Aguirre Mayorga, Santiago
In this paper, a reference model is proposed for the evaluation of operational processes variables in healthcare using process mining and data visualization techniques. For this reason, the PM2 methodology is used as a reference to conduct projects oriented to the evaluation of data collected in business processes, including data visualization techniques, with the purpose of reducing the acquisition time of knowledge related to the processes of institutions of the healthcare sector. The proposed model is based on the application of a set of data visualization techniques to reduce the knowledge acquisition gap presented by process mining. The model consists of 5 stages: 1. Extraction, 2. Event processing, 3. Process mining, 4. Data visualization and 5. Evaluation of results. A testing scenario was defined in a Clinic network in Lima (Peru) to validate the proposed model and the surgery process was chosen, since it is critical for the organization. The results showed the existing bottleneck in the surgery process, between the activities of registering and preparing the patient. This allowed to take corrective measures between the activities to optimize the process cycle time. Likewise, a sequence was identified in the activities that had not been previously detected in the process documentation; these represented 2.6% difference, so the documented process was modified to achieve a 99.6% affinity.
Predictive analysis for calculating the valuation of the affiliated fund of a private pension system using machine learning techniques and tools
(LACCEI, Inc., 2019-07) Armas, Jimmy; Espinoza Ladera, Jhonatan; Dueñas Castillo, Brian; Aguirre Mayorga, Santiago
This paper proposes a model for the analysis of the prediction of the accumulated affiliated fund based on an area of study of machine learning. The model allows to project the pension fund of an affiliate to the private pension system by means of a web solution, in order that people have information and adequate tools that allow them to have a general vision of the valuation of their funds over the years until the time of retirement. In Peru, the decree of law 1990 indicates that the year of retirement is 65 years, although there is also the figure of early retirement. The proposed model consists of the use of data analytics based on the modeling of machine learning algorithms through cloud platforms. The structure of the model includes four layers: the transformation of the affiliate's data, the security and privacy of the personal data, obtaining and management of data, and finally, the life cycle of the data applied to the analytics. The model emphasizes data analytics concepts where large amounts of data are examined that lead to conclusions for better decision making. For this, the machine learning technique "boosted decision tree" is used due to the proximity of this technique applied in the financial projections. The model was validated with a pension fund administrator (AFP) in Lima (Peru) and the results obtained focused on the use of improved decision tree regression with a coefficient of determination of 99.997% and an average square error of 0.00650%. The coefficient of determination is an indicator of the quality of the model to predict results while the quadratic error quantifies the percentage of error among the set of results obtained under the boosted decision tree regression model.