An Emergency Unit Support System to Diagnose Chronic Heart Failure Embedded with SWRL and Bayesian Network

Al-Hamadani, Baydaa

doi:10.14569/ijacsa.2016.070761

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…An evaluative analysis between DT and KNN has been provided. The auto-encoder performs feature extraction, minimising the number of attributes needed to portray the heart disease dataset [19]. Another medical IoT-based diagnostic system [20] was proposed by the same author that detects people suffering from the early stages of breast cancer.…”

Section: Related Workmentioning

confidence: 99%

Design and Development of IoT and Deep Ensemble Learning Based Model for Disease Monitoring and Prediction

et al. 2023

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With the rapidly increasing reliance on advances in IoT, we persist towards pushing technology to new heights. From ordering food online to gene editing-based personalized healthcare, disruptive technologies like ML and AI continue to grow beyond our wildest dreams. Early detection and treatment through AI-assisted diagnostic models have outperformed human intelligence. In many cases, these tools can act upon the structured data containing probable symptoms, offer medication schedules based on the appropriate code related to diagnosis conventions, and predict adverse drug effects, if any, in accordance with medications. Utilizing AI and IoT in healthcare has facilitated innumerable benefits like minimizing cost, reducing hospital-obtained infections, decreasing mortality and morbidity etc. DL algorithms have opened up several frontiers by contributing towards healthcare opportunities through their ability to understand and learn from different levels of demonstration and generalization, which is significant in data analysis and interpretation. In contrast to ML which relies more on structured, labeled data and domain expertise to facilitate feature extractions, DL employs human-like cognitive abilities to extract hidden relationships and patterns from uncategorized data. Through the efficient application of DL techniques on the medical dataset, precise prediction, and classification of infectious/rare diseases, avoiding surgeries that can be preventable, minimization of over-dosage of harmful contrast agents for scans and biopsies can be reduced to a greater extent in future. Our study is focused on deploying ensemble deep learning algorithms and IoT devices to design and develop a diagnostic model that can effectively analyze medical Big Data and diagnose diseases by identifying abnormalities in early stages through medical images provided as input. This AI-assisted diagnostic model based on Ensemble Deep learning aims to be a valuable tool for healthcare systems and patients through its ability to diagnose diseases in the initial stages and present valuable insights to facilitate personalized treatment by aggregating the prediction of each base model and generating a final prediction.

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Section: Related Workmentioning

confidence: 99%

Design and Development of IoT and Deep Ensemble Learning Based Model for Disease Monitoring and Prediction

et al. 2023

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“…A literature review on different approaches was performed to identify studies about CVD using machine learning techniques such as BN, PA and SEM. The authors [17][18][19][20][21][22][23][24] developed models using Bayesian network techniques. PA models were created in the studies [25][26][27].…”

Section: Introductionmentioning

confidence: 99%