Application of ensemble machine learning algorithms on lifestyle factors and wearables for cardiovascular risk prediction

Huang, Weiting; Tan, Wei Ying; Chin, Woon Loong Calvin; Baskaran, Lohendran; Marcus, Ong Eng Hock; Yeo, Khung Keong; Kiong, Ng See

doi:10.1038/s41598-021-04649-y

Cited by 29 publications

(24 citation statements)

References 52 publications

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“…However, the Random Forest algorithm takes only a subspace of attributes into account when splitting a node. Trees can be more random when looking for the importance of the attribute randomly [45].…”

Section: Fig2 Random Forest Model With Three Treesmentioning

confidence: 99%

Classification and Diagnosis of Heart Disease Using Machine Learning

Mohsen‬‏,

Naoufel,

Alrashahy

et al. 2024

Preprint

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Heart disease is one of the most common causes of death worldwide, and accurate and rapid diagnosis is needed to avoid serious complications. This study aimed to present a system for diagnosing and classifying common heart diseases using machine learning techniques. The data used are a set of medical records for heart patients at Ibb Medical Clinic, Ibb City, Yemen. Fourteen different classification algorithms were implemented using the Weka tool to classify different heart diseases, including eight heart diseases. The results showed that the RF algorithm was the best in terms of accuracy, sensitivity, specification, and F-measure. Based on this algorithm, a web system based on the ASPX language was designed to help users enter patient data and obtain a diagnosis and classification of heart disease. The system can help doctors in the diagnosis process and increase the medical awareness of the community.

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Section: Fig2 Random Forest Model With Three Treesmentioning

confidence: 99%

Classification and Diagnosis of Heart Disease Using Machine Learning

Mohsen‬‏,

Naoufel,

Alrashahy

et al. 2024

Preprint

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show abstract

“…However, the random forest method takes only a subspace of attributes into account when splitting a node. Trees can be more random when looking for the importance of the attribute randomly [45].…”

Section: Fig2 Random Forest Model With Three Treesmentioning

confidence: 99%

Classification and Diagnosis of Heart Disease Using Machine Learning

Mohsen‬‏,

Naoufel,

Alrashahy

et al. 2024

Preprint

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Heart disease is a common and serious disease that causes many deaths around the world. The study aims to explore the use of machine learning techniques in classifying and diagnosing heart diseases and to develop a system capable of diagnosing and classifying different types of heart diseases using machine learning techniques. A number of algorithms commonly used in healthcare, such as Naive Bayes model, SVM, k-nearest neighbor (k-NN), and others, were reviewed. The study points out the importance of the quality of the data used in the database to obtain an accurate and reliable diagnosis. Data were collected from patient records in hospitals and clinics, analyzed and compared with previous relevant studies. Clinical decision assistance software has been used to help make medical decisions based on patient information. Positive results have been achieved that confirm the effectiveness of using machine learning techniques in diagnosing heart diseases. These technologies have shown the potential to improve the accuracy and efficiency of diagnosis, leading to improved patient outcomes and reduced health burdens. It also concluded the need to develop effective diagnostic tools and enhance the prevention of heart disease. The study is an important foundation for healthcare professionals and doctors working in the field of cardiology, as the techniques used can help them better understand and diagnose conditions and improve patient care.

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“…The wearable devices have a reported sensitivity of more than 90% for detecting seizures [ 106 ]. Weiting et al used several algorithms, including SVM, RF, and naive Bayes, to build an ML algorithm ensemble for the purpose of predicting the cardiovascular risk from wearable healthcare data-collection devices [ 107 ]. In a study involving 407 participants using smartwatches, a gradient-boosting algorithm identified and predicted SARS-CoV2 infections [ 108 ].…”

Section: Role Of Machine Learning In Diagnosticsmentioning

confidence: 99%

Smart Consumer Wearables as Digital Diagnostic Tools: A Review

Chakrabarti¹,

Biswas²,

Jones³

et al. 2022

Diagnostics

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The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as personalized digital diagnostic tools. Data from such devices have enabled the prediction and detection of various physiological as well as psychological conditions and diseases. In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic disorders, including diabetes, sleep quality, and psychological illnesses. The fruitful usage of wearables requires fast and insightful data analysis, which is feasible through machine learning. In this review, we have also discussed various machine-learning applications and outcomes for wearable data analyses. Finally, we have discussed the current challenges with wearable usage and data, and the future perspectives of wearable devices as diagnostic tools for research and personalized healthcare domains.

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Application of ensemble machine learning algorithms on lifestyle factors and wearables for cardiovascular risk prediction

Cited by 29 publications

References 52 publications

Classification and Diagnosis of Heart Disease Using Machine Learning

Classification and Diagnosis of Heart Disease Using Machine Learning

Classification and Diagnosis of Heart Disease Using Machine Learning

Smart Consumer Wearables as Digital Diagnostic Tools: A Review

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