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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Author: Armas, Jimmy

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Now showing 1 - 5 of 5
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    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.
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    Security model to protect patient data in mHealth systems through a Blockchain network
    (LACCEI, Inc., 2019-07) Armas, Jimmy; Natividad Peña, Cristhian Alexis; Gutiérrez Díaz, Angel Elí; Madrid Molina, Juan Manuel
    On this research paper we propose a security model to protect patient data on mobile health systems (mHealth) through a Blockchain network. This model is implemented under a Blockchain platform that allows collecting, sharing and integrating data in a safe way through a mobile app for mHealth devices, for medical care in Peruvian clinics. This security model consists of three stages: 1. Data collection, 2. Data processing, 3. System monitoring. It should be noted that the patient is autonomous in the management of his information, and that each user requires a single identifier to get access to the data. A test scenario was defined to validate the proposed model. Also, the study was conducted with a group of users through a health mobile app and the used medical data was provided by a hospital in Peru as anonymized research data. During the study, we validated the following topics: access control to the network, access to medical information of authorized users, data integrity on each transaction and performance evaluation of the system under a high user transaction load. Preliminary results show the system average response time is 4.72 seconds for 10,000 users carrying out requests simultaneously.
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    A Frugal Technological Model for Leukemia Detection Using Digital Microscopy
    (LACCEI, Inc., 2019-07) Armas, Jimmy; López Prieto, Juan; Purizaca Pérez, Miguel; Gonzalez, Paola
    In this paper, we propose a low-cost technological model for optimizing the detection process of leukemia using digital microscopy. The model is targeted to be used in remote areas in developing countries where availability of resources is an issue. This model applies the canny algorithm on a bank of images of normal and abnormal microscopic cells. The proposed model includes the capture, digitization, and analysis of microscopic samples, Fives phases are included in this model: 1. Data collection; 2. Data capture; 3. Image processing; 4. Cell classification; 5. Display of results. The model was preliminary validated with five blood samples from three men and two women in different age categories. All these samples were validated by the Head of Clinical Pathology at a public hospital in Callao, Lima, Peru. The results showed that a 90.5% effectiveness rate of white blood cell identification was obtained. This results aims to provide an additional and accurate tool to detection of potential leukemia.
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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.
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    A technological platform using serious game for children with Autism Spectrum Disorder (ASD) in Peru
    (LACCEI, Inc., 2019-07) Armas, Jimmy; Bonifaz Pedreschi, Vanessa; Ospina Díaz, Diego André; Gonzalez, Paola
    Children with high-functioning ASD struggle with recognizing and expressing their emotions. Serious games, computerized intervention programs, have successfully been used in the treatment of this disorder. In this paper, we proposed an enhanced and comprehensive technological platform using serious games to optimize the process of emotional and social learning therapy in treating children with ASD. This platform consists of four phases: Patient Registration, Data Transmission, Reporting, and Analysis. The platform was validated and tested in an educational and behavioral therapy institute in Peru. 20 children between ages 3 to 10 years old participated in the study. Children were tested before and after using the SG. The preliminary results showed a significant improvement in emotion recognition after using the SG. The therapists also reported their satisfaction with the reporting aspect of the platform.