A Machine-Learning-Based System for Prediction of Cardiovascular and Chronic Respiratory Diseases

Shah, Wajid; Aleem, Muhammad; Iqbal, Muhammad Azhar; Islam, Muhammad Arshad; Ahmed, Usman; Srivastava, Gautam; Lin, Jerry Chun‐Wei

doi:10.1155/2021/2621655

Cited by 13 publications

(7 citation statements)

References 27 publications

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“…Cardiovascular Disease: AI algorithms process data derived from wearables, electronic health records, and other sources to reveal deviations in a person's heart rate and activity metrics [53]. These discrepancies indicate the onset of cardiovascular disease.…”

Section: Early Detection Of Chronic and Infectious Diseasesmentioning

confidence: 99%

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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Section: Early Detection Of Chronic and Infectious Diseasesmentioning

confidence: 99%

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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“…Then, the observation windowing time is moved forward every several minutes or hours to collect time series of vital signs monitoring data as samples. In the critical condition, the windowing time frame could be less than 3 min or 60 s [52].…”

Section: Research Question 3: How To Develop the ML For Prediction/de...mentioning

confidence: 99%

Mechanical ventilation intervention based on machine learning from vital signs monitoring: a scoping review

Baidillah

Busono

Riyanto

2023

Meas. Sci. Technol.

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BackgroundAsynchronous breathing (AB) during mechanical ventilation (MV) may lead to a detrimental effect on the patient's condition. Due to the massive amount of data displayed in a large ICU, a machine learning algorithm (MLA) was proposed extensively to extract the patterns within the multiple continuous-in-time vital signs, to determine which are the variables that will predict the AB, to intervene in the MV as an early warning system, and finally to replace a highly demand of clinician's cognition. ObjectiveThis study reviews the MLA for prediction and detection models from vital signs monitoring data for MV intervention. MethodsPublication on MLA development on MV intervention based on vital signs monitoring to support clinicians' decision-making process was extracted from the three electronic academic research databases Web of Science Core Collection (WoSCC), ScienceDirect, and PUBMED Central to February 2023. Findings838 papers from the electronic academic research databases are extracted. There are 14 review papers, while 25 related papers that pass with the Quality Assessments (QA). ConclusionsFew studies have been published that considered VS monitoring data along with the MV parameters waveforms for MV intervention. Vital signs monitoring data is not the only predictor in the developed MLA. Most studies suggested that developing the MLA for direct MV intervention requires more concern in the pre-processing of real-time data to avoid false positive and false detection than developing MLA itself. 

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“…The accuracy of J48 in brucellosis prediction is contingent upon the quality of the dataset and the selection of relevant attributes 20 . With its ability to learn and classify based on patterns and relationships, J48 provides valuable insights for early detection and control of brucellosis, assisting in the prevention and management of this infectious disease 21 . RESULT AND DISCUSSION…”

Section: Multilayer Perceptronmentioning

confidence: 99%

Application of Machine Learning for Predicting Brucellosis Disease in Dairy Cattle

Hussaini,

Tito,

Hasan

et al. 2023

Preprint

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Brucellosis, a zoonotic disease caused by Brucella bacteria, has emerged as a significant concern in Bangladesh, affecting both animals and humans, with economic repercussions and health risks. This paper addresses the urgent need for effective disease management in a country where agriculture and livestock are pivotal to the economy. The study focuses on identifying risk factors for brucellosis in dairy cattle and their correlation with factors collected from Central Cattle Breeding and Dairy Farm (CCBDF) and Military Dairy Farms (MDF) in Bangladesh. Serum and milk samples, along with demographic data, were collected and analyzed. To address the challenge of imbalanced data, the study introduces the application of the "SMOTE" Weka filter for the first time in Bangladesh's veterinary profession. This balances the dataset, enhancing the accuracy of predictive modeling. Machine learning models, specifically Multilayer Perceptron (MLP) and J48 decision tree algorithms, are employed for brucellosis prediction. MLP achieved a Correct Classification (CC) rate of 95.0801%, while J48 achieved 94.0503%, demonstrating their efficacy. Sensitivity analysis identifies key attributes, with retained placenta being the most significant, aiding in prioritizing control measures. Association Rules reveal patterns and relationships among disease-related factors, enhancing understanding and proactive prevention.

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A Machine-Learning-Based System for Prediction of Cardiovascular and Chronic Respiratory Diseases

Cited by 13 publications

References 27 publications

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

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

Mechanical ventilation intervention based on machine learning from vital signs monitoring: a scoping review

Application of Machine Learning for Predicting Brucellosis Disease in Dairy Cattle

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