Using Artificial Intelligence for the Specification of m-Health and e-Health Systems

Lano, Kevin; Tehrani, Sobhan Y.; Umar, M. Sarosh; Alwakeel, Lyan

doi:10.1007/978-3-030-99838-7_15

Cited by 3 publications

(1 citation statement)

References 12 publications

(13 reference statements)

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“…In addition, the Convolutional Neural Network (CNN) based deep learning classifier has been utilized to detect the leukemia cancer using the blood film input. Lano, et al [19] utilized an AI mechanism to assist the m-health application systems. Here, the machine learning model is utilized to analyze the behavior elements of the medical systems.…”

Section: Related Workmentioning

confidence: 99%

AI based Dynamic Prediction Model for Mobile Health Application System

Ramesh¹,

Subbaraya²,

Kumar³

2023

IJACSA

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In recent decades, mobile health (m-health) applications have gained significant attention in the healthcare sector due to their increased support during critical cases like cardiac disease, spinal cord problems, and brain injuries. Also, m-health services are considered more valuable, mainly where facilities are deficient. In addition, it supports wired and advanced wireless technologies for data transmission and communication. In this work, an Artificial Intelligence (AI)based deep learning model is implemented to predict healthcare data, where the data handling is performed to improve the dynamic prediction performance. It includes the working modules of data collection, normalization, AI-based classification, and decision-making. Here, the m-health data are obtained from the smart devices through the service providers, which comprises the health information related to blood pressure, heart rate, glucose level, etc. The main contribution of this paper is to accurately predict Cardio Vascular Disease (CVD) from the patient dataset stored in cloud using the AIbased m-health system. After obtaining the data, preprocessing can be performed for noise reduction and normalization because prediction performance highly depends on data quality. Consequently, we use the Gorilla Troop Optimization Algorithm (GTOA) to select the most relevant functions for classifier training and testing. Classify his CVD type according to a selected set of features using bidirectional long-term memory (Bi-LSTM). Moreover, the proposed AI-based prediction model's performance is validated and compared using different measures.

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Section: Related Workmentioning

confidence: 99%

AI based Dynamic Prediction Model for Mobile Health Application System

Ramesh¹,

Subbaraya²,

Kumar³

2023

IJACSA

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Forecasting Emergency Department in Hospitals Using Regression Prediction Models

Ramesh,

Subbaraya,

Ravi Kumar

2024

2024 International Conference on Advances in Modern Age Technologies for Health and Engineering Science (AMATHE)

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AI based Dynamic Prediction Model for Mobile Health Application System

Ramesh¹,

Subbaraya²,

Kumar³

2023

IJEAT

View full text Add to dashboard Cite

In recent decades, mobile health (m-health) applications have gained significant attention in the healthcare sector due to their increased support during critical cases like cardiac disease, spinal cord problems, and brain injuries. Also, m-health services are considered more valuable, mainly where facilities are deficient. In addition, it supports wired and advanced wireless technologies for data transmission and communication. In this work, an AI-based deep learning model is implemented to predict healthcare data, where the data handling is performed to improve the prediction performance. It includes the working modules of data collection, normalization, AI-based classification, and decision-making. Here, the m-health data are obtained from the smart devices through the service providers, which comprises the health information related to blood pressure, heart rate, glucose level, etc. The main contribution of this paper is to accurately predict Cardio Vascular Disease (CVD) from the patient dataset using the AI-based m-health system. After obtaining the data, preprocessing can be performed for noise reduction and normalization because prediction performance highly depends on data quality. Consequently, We use the Gorilla Troop Optimization Algorithm (GTOA) to select the most relevant functions for classifier training and testing. Classify his CVD type according to a selected set of features using bidirectional long-term memory (Bi-LSTM). Moreover, the proposed AI-based prediction model's performance is validated and compared using different measures.

show abstract

Using Artificial Intelligence for the Specification of m-Health and e-Health Systems

Cited by 3 publications

References 12 publications

AI based Dynamic Prediction Model for Mobile Health Application System

AI based Dynamic Prediction Model for Mobile Health Application System

Forecasting Emergency Department in Hospitals Using Regression Prediction Models

AI based Dynamic Prediction Model for Mobile Health Application System

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