Record Details

<p>An Industry Framework for Remote Health Monitoring using Machine Learning Models to Predict a Disease</p>

Online Publishing @ NISCAIR

View Archive Info
 
 
Field Value
 
Authentication Code dc
 
Title Statement <p>An Industry Framework for Remote Health Monitoring using Machine Learning Models to Predict a Disease</p>
 
Added Entry - Uncontrolled Name Venkateswarulu, N ; Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad 500 075, Telangana, India
Manike, Chiranjeevi ; Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad 500 075, Telangana, India
Obulesu, O ; Department of Computer Science and Engineering, G Narayanamma Institute of Technology & Science, Hyderabad 500104, Telangana, India
 
Uncontrolled Index Term Artificial intelligence, MP-ND, Smart devices, U-CSO
 
Summary, etc. <p>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.</p>
 
Publication, Distribution, Etc. Journal of Scientific & Industrial Research
2023-02-09 21:08:13
 
Electronic Location and Access application/pdf
http://op.niscair.res.in/index.php/JSIR/article/view/70221
 
Data Source Entry Journal of Scientific & Industrial Research; ##issue.vol## 82, ##issue.no## 02 (2023)
 
Language Note en