Predictive analysis effectiveness in determining the epidemic disease infected area

Background: Artificial intelligence techniques are widely used in solving medical problems. Recently, researchers have used various deep learning techniques for the severity classification of Chikungunya disease. But these techniques suffer from overfitting and hyper-parameters tuning problems. Methods: In this paper, an artificial intelligence-based cyber-physical system (CPS) is proposed for the severity classification of Chikungunya disease. In CPS system, the physical components are integrated with computational algorithms to provide better results. Random forest (RF) is used to design the severity classification model for Chikungunya disease. However, RF suffers from overfitting and poor computational speed problems due to complex architectures and large amounts of connection weights. Therefore, an evolving RF model is proposed using the adaptive crossover-based genetic algorithm (ACGA). Results: ACGA can efficiently optimize the architecture of RF to achieve better results with better computational speed. Extensive experiments are performed by utilizing the Chikungunya disease dataset. Conclusion: Performance analysis demonstrates that ACGA-RF achieves higher performance as compared to the competitive models in terms of F-measure, accuracy, sensitivity, and specificity. The proposed CPS system can prevent users from visiting hospitals and can render services to patients living far away from hospitals.

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“…Ibrahim et al [17] presented the backpropagation method for predicting the epidemic disease. They used epidemic disease factors for prediction.…”

Section: Related Workmentioning

confidence: 99%

Artificial Intelligence-Based Cyber-Physical System for Severity Classification of Chikungunya Disease

Singh

Kaur

Kumar

et al. 2022

IEEE J. Transl. Eng. Health Med.

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“…For example, time series data can be used in a machine learning setup to predict future disease trends. When predicting diseases, some of the factors fed into machine learning algorithms include population density, hotspots, vaccination levels, clinical case classifications, and geomapping [57]. Accordingly, IoT devices that could be used include satellites and drones to capture population densities and other forms of geography-related data.…”

Section: Epidemic Outbreak Predictionmentioning

confidence: 99%

Trends in Using IoT with Machine Learning in Health Prediction System

2021

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Machine learning (ML) is a powerful tool that delivers insights hidden in Internet of Things (IoT) data. These hybrid technologies work smartly to improve the decision-making process in different areas such as education, security, business, and the healthcare industry. ML empowers the IoT to demystify hidden patterns in bulk data for optimal prediction and recommendation systems. Healthcare has embraced IoT and ML so that automated machines make medical records, predict disease diagnoses, and, most importantly, conduct real-time monitoring of patients. Individual ML algorithms perform differently on different datasets. Due to the predictive results varying, this might impact the overall results. The variation in prediction results looms large in the clinical decision-making process. Therefore, it is essential to understand the different ML algorithms used to handle IoT data in the healthcare sector. This article highlights well-known ML algorithms for classification and prediction and demonstrates how they have been used in the healthcare sector. The aim of this paper is to present a comprehensive overview of existing ML approaches and their application in IoT medical data. In a thorough analysis, we observe that different ML prediction algorithms have various shortcomings. Depending on the type of IoT dataset, we need to choose an optimal method to predict critical healthcare data. The paper also provides some examples of IoT and machine learning to predict future healthcare system trends.

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“…In [3], a solution for medical imaging applications was developed using next-generation methods for federated, secure and privacy-preserving artificial intelligence. A predictive analysis method for epidemic diseases to identify and locate infected areas by using a back-propagation method was introduced in [4]. The solution-focused on classifying the epidemic disease spreading factors as the elements for weight adjustment on the prediction of epidemic disease occurrences.…”

Section: Epidemic Disease Management Through Intelligent Learningmentioning

confidence: 99%

Preventing and Controlling Epidemics through Blockchain-Assisted AI-Enabled Networks

Otoum¹,

Ridhawi²,

Mouftah³

2021

Preprint

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The COVID-19 pandemic, which spread rapidly in late 2019, has revealed that the use of computing and communication technologies provides significant aid in preventing, controlling, and combating infectious diseases. With the ongoing research in nextgeneration networking (NGN), the use of secure and reliable communication and networking is of utmost importance when dealing with users' health records and other sensitive information. Through the adaptation of Artificial Intelligence (AI)-enabled NGN, the shape of healthcare systems can be altered to achieve smart and secure healthcare capable of coping with epidemics that may emerge at any given moment. In this article, we envision a cooperative and distributed healthcare framework that relies on state-of-the-art computing, communication, and intelligence capabilities, namely, Federated Learning (FL), mobile edge computing (MEC), and Blockchain, to enable epidemic (or suspicious infectious disease) discovery, remote monitoring, and fast health-authority response. The introduced framework can also enable secure medical data exchange at the edge and between different health entities. Such a technique, coupled with the low latency and high bandwidth functionality of 5G and beyond networks, would enable mass surveillance, monitoring and analysis to occur at the edge. Challenges, issues, and design guidelines are also discussed in this article with highlights on some trending solutions.

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Predictive analysis effectiveness in determining the epidemic disease infected area

Cited by 13 publications

References 8 publications

Artificial Intelligence-Based Cyber-Physical System for Severity Classification of Chikungunya Disease

Artificial Intelligence-Based Cyber-Physical System for Severity Classification of Chikungunya Disease

Trends in Using IoT with Machine Learning in Health Prediction System

Preventing and Controlling Epidemics through Blockchain-Assisted AI-Enabled Networks

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