An Ensemble Model for Predicting Chronic Diseases Using Machine Learning Algorithms

Manjulatha, B.; Pabboju, Suresh

doi:10.1007/978-981-16-1502-3_34

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…Several studies have investigated the use of AI for the early detection of chronic diseases such as cardiovascular disease, diabetes, cancer, and infectious diseases [14,15,[45][46][47][48][49][50][51][52]. These research efforts highlight the ability of AI to significantly improve public health outcomes by allowing health professionals to detect chronic diseases in their early stages when interventions are most effective.…”

Section: Early Detection Of Chronic and Infectious Diseasesmentioning

confidence: 99%

“…These models are suitable for binary classification tasks that involve predicting the presence or absence of a particular health state based on patient data. Ensemble methods, such as random forest and Gradient Boosting, have gained popularity due to their ability to combine multiple base models to improve prediction accuracy [46,47,69,70]. They have been applied in the context of early health prediction to capture complex interactions in health data.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

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Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions

Badidi

2023

Future Internet

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Edge AI, an interdisciplinary technology that enables distributed intelligence with edge devices, is quickly becoming a critical component in early health prediction. Edge AI encompasses data analytics and artificial intelligence (AI) using machine learning, deep learning, and federated learning models deployed and executed at the edge of the network, far from centralized data centers. AI enables the careful analysis of large datasets derived from multiple sources, including electronic health records, wearable devices, and demographic information, making it possible to identify intricate patterns and predict a person’s future health. Federated learning, a novel approach in AI, further enhances this prediction by enabling collaborative training of AI models on distributed edge devices while maintaining privacy. Using edge computing, data can be processed and analyzed locally, reducing latency and enabling instant decision making. This article reviews the role of Edge AI in early health prediction and highlights its potential to improve public health. Topics covered include the use of AI algorithms for early detection of chronic diseases such as diabetes and cancer and the use of edge computing in wearable devices to detect the spread of infectious diseases. In addition to discussing the challenges and limitations of Edge AI in early health prediction, this article emphasizes future research directions to address these concerns and the integration with existing healthcare systems and explore the full potential of these technologies in improving public health.

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