Java Servlets

Poo, Danny Chiang Choon; Kiong, Derek; Ashok, Swarnalatha

doi:10.1007/978-1-84628-963-7_15

Cited by 1 publication

(1 citation statement)

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“…The performance of the classifier can be measured with error performance and the coefficient of determination (R 2 ). This analysis is coded with an API of the Waikato Environment for Knowledge Analysis (WEKA) [49], Java-based ML software implemented in the Apache Spark development environment [50], Java Servlet (JS), and Java Server Pages (JSP) [51] for handling the data entries of the microservices. The Java web service is in Apache Maven [52].…”

Section: Analysis By Machine Learning Classifiersmentioning

confidence: 99%

Medical Prognosis of Infectious Diseases in Nursing Homes by Applying Machine Learning on Clinical Data Collected in Cloud Microservices

Garcés-Jiménez

Calderón-Gómez

Pulido

et al. 2021

IJERPH

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Background: treating infectious diseases in elderly individuals is difficult; patient referral to emergency services often occurs, since the elderly tend to arrive at consultations with advanced, serious symptoms. Aim: it was hypothesized that anticipating an infectious disease diagnosis by a few days could significantly improve a patient’s well-being and reduce the burden on emergency health system services. Methods: vital signs from residents were taken daily and transferred to a database in the cloud. Classifiers were used to recognize patterns in the spatial domain process of the collected data. Doctors reported their diagnoses when any disease presented. A flexible microservice architecture provided access and functionality to the system. Results: combining two different domains, health and technology, is not easy, but the results are encouraging. The classifiers reported good results; the system has been well accepted by medical personnel and is proving to be cost-effective and a good solution to service disadvantaged areas. In this context, this research found the importance of certain clinical variables in the identification of infectious diseases. Conclusions: this work explores how to apply mobile communications, cloud services, and machine learning technology, in order to provide efficient tools for medical staff in nursing homes. The scalable architecture can be extended to big data applications that may extract valuable knowledge patterns for medical research.

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Section: Analysis By Machine Learning Classifiersmentioning

confidence: 99%