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An Industry Framework for Remote Health Monitoring using Machine Learning Models to Predict a Disease

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Title An Industry Framework for Remote Health Monitoring using Machine Learning Models to Predict a Disease
 
Creator Venkateswarulu, N
Manike, Chiranjeevi
Obulesu, O
 
Subject Artificial intelligence
MP-ND
Smart devices
U-CSO
 
Description 232-240
Remote health monitoring frameworks gained significant attention due to their real intervention and treatment standards.
Most conventional works object to developing remote monitoring frameworks for identifying the disease at the earlier stages
for an appropriate diagnosis. Still, it faced the problems with complexity in operations, increased cost of resources,
misprediction results, which requires more time consumption for data gathering, and reduced convergence rate. Hence, the
proposed work intends to design a machine learning based remote health monitoring framework for predicting heart disease
and diabetes from the given medical datasets. In this framework, the Industry based smart devices are used to gather the
health information of patients, and the obtained information is integrated together by using different nodes that includes the
detecting node, visualization node, and prognostic node. Then, the medical dataset preprocessing is performed to normalize
the attributes by identifying the missing values and eliminating the irrelevant qualities. Consequently, the Unified Levy
Modeled Crow Search Optimization (U-CSO) algorithm is employed to select the optimal features based on the global
fitness function, which helps increase the accuracy and reduce the training time of the classifier. Finally, the Most
Probabilistic Guided Naïve Distribution (MP-ND) based classification model is utilized for predicting the label as to
whether normal or disease affected. During an evaluation, two different datasets, such as PIMA and Hungarian, are used to
validate and compare the results of the proposed model by using various performance measures. A Patients' health status can
be monitored remotely for disease detection and proper diagnosis.
 
Date 2023-02-08T05:15:29Z
2023-02-08T05:15:29Z
2023-02
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/61361
https://doi.org/10.56042/jsir.v82i2.70221
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source JSIR Vol.82(02) [February 2023]