MATHS: Machine Learning Techniques in Healthcare System

Chugh, Medha; Johari, Rahul; Goel, Anmol

doi:10.1007/978-981-16-3071-2_56

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…In [113], The author proposed a healthcare management system that patients could use to schedule appointments with doctors and verify their prescriptions. It gives support for ML to detect ailments and determine medicines.…”

Section: Ml-based Healthcare Predictionmentioning

confidence: 99%

Healthcare Predictive Analytics Using Machine Learning and Deep Learning Techniques: A Survey

Badawy

Ramadan

Hefny

2022

Preprint

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Aim This paper aims to present a comprehensive survey of existing machine learning and deep learning approaches utilized in healthcare prediction, as well as identify inherent obstacles to applying these approaches in the healthcare prediction domain. Background Healthcare prediction has been a significant factor in saving human lives in recent years. In the domain of healthcare, there is a rapid development of intelligent systems for analyzing complicated relationships among data and transforming them into real information for use in the prediction process. Consequently, artificial intelligence is rapidly transforming the healthcare industry. Thus comes the role of systems depending on machine learning as well as deep learning in the creation of steps that diagnose and predict diseases, whether from clinical data or based on images, that provide tremendous clinical support by simulating human perception and can even diagnose diseases that are difficult to detect by human intelligence. Methods The studies discussed in this paper have been presented in journals published by IEEE, Springer, and Elsevier. Machine learning, deep learning, healthcare, surgery, cardiology, radiology, hepatology, and nephrology are some of the terms used to search for these studies. The studies chosen for this survey are concerned with the use of machine learning as well as deep learning algorithms in healthcare prediction. Results A total of 40 working papers were selected and the methodology for each paper was clarified. Conclusion This paper presents a comprehensive survey as well as the current challenges in healthcare prediction. studies have shown that artificial intelligence plays a significant role in diseases diagnosing.

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Section: Ml-based Healthcare Predictionmentioning

confidence: 99%

Healthcare Predictive Analytics Using Machine Learning and Deep Learning Techniques: A Survey

Badawy

Ramadan

Hefny

2022

Preprint

View full text Add to dashboard Cite

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Healthcare predictive analytics using machine learning and deep learning techniques: a survey

Badawy,

Ramadan,

Hefny

2023

Journal of Electrical Systems and Inf Technol

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Healthcare prediction has been a significant factor in saving lives in recent years. In the domain of health care, there is a rapid development of intelligent systems for analyzing complicated data relationships and transforming them into real information for use in the prediction process. Consequently, artificial intelligence is rapidly transforming the healthcare industry, and thus comes the role of systems depending on machine learning and deep learning in the creation of steps that diagnose and predict diseases, whether from clinical data or based on images, that provide tremendous clinical support by simulating human perception and can even diagnose diseases that are difficult to detect by human intelligence. Predictive analytics for healthcare a critical imperative in the healthcare industry. It can significantly affect the accuracy of disease prediction, which may lead to saving patients' lives in the case of accurate and timely prediction; on the contrary, in the case of an incorrect prediction, it may endanger patients' lives. Therefore, diseases must be accurately predicted and estimated. Hence, reliable and efficient methods for healthcare predictive analysis are essential. Therefore, this paper aims to present a comprehensive survey of existing machine learning and deep learning approaches utilized in healthcare prediction and identify the inherent obstacles to applying these approaches in the healthcare domain.

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A Comprehensive Study of Deep Learning Techniques to Predict Dissimilar Diseases in Diabetes Mellitus Using IoT

Balaraju,

Lakshmanna

2024

RACSC

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India has evaluated 77 million people with diabetes, which makes it the second most elaborated disease in the world. Diabetes is a chronic syndrome that occurs with increased sugar levels in the blood cells. Once diabetes is diagnosed and untreated by physicians, it may affect the internal organs slowly, so there is a necessity for early prediction. Popular Machine Learning (ML) techniques existed for the early prediction of diabetes mellitus. A significant perspective is to be considered in total management by machine learning algorithms, but it is not a good enough model to predict DMT2. Therefore, Deep learning (DL) models are utilized to produce enhanced prediction accuracy. The ML methods are evaluated and analyzed distinctly on the inconspicuous test information. DL is a subpart of ML with many data sets recurrently used to train the system. IoT was another emerging technology-based Healthcare Monitoring System (HMS) built to support the vision of patients and doctors in the healthcare domain. This paper aims to survey ML and DL techniques relevant to Dissimilar Disease prediction in Diabetes Mellitus. Finally, by doing a study on it, deep learning methods performed well in predicting the dissimilar diseases related to diabetes and also other disease predictions using m-IoT devices. This study will contribute to future deep-learning ideas that will assist in detecting diabetic-related illnesses with greater accuracy.

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MATHS: Machine Learning Techniques in Healthcare System

Cited by 3 publications

References 8 publications

Healthcare Predictive Analytics Using Machine Learning and Deep Learning Techniques: A Survey

Healthcare Predictive Analytics Using Machine Learning and Deep Learning Techniques: A Survey

Healthcare predictive analytics using machine learning and deep learning techniques: a survey

A Comprehensive Study of Deep Learning Techniques to Predict Dissimilar Diseases in Diabetes Mellitus Using IoT

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