Rapid Detection of Heart Rate Fragmentation and Cardiac Arrhythmias: Cycle-by-Cycle rr Analysis, Supervised Machine Learning Model and Novel Insights

Rajagopalan, Ananya; Vollmer, Marcus

doi:10.1007/978-3-030-21642-9_47

Cited by 1 publication

(2 citation statements)

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“…The dataset used for the development of the model was collected from an urban hospital in Boston and five models were developed using SVM, AdaBoost, LR, Naïve Bayes, and likelihood ratio test algorithms. A supervised ML model for rapid detection of heat rate fragmentation and cardiac arrhythmias was developed in the study of [24]. A random forest algorithm and a dataset of 300 instances of arrhythmic, non-arrhythmic coronary artery disease, and individuals without any medically significant cardiac conditions were used to develop a predictive model.…”

Section: Related Workmentioning

confidence: 99%

“…The study further found that supervised machine learning is an important and underutilized technique that has considerable potential for the evolutionary genomics. A supervised ML model for the identification of mosquitoes from the backscattered optical signal was developed in the study of [24]. The study showed that the optical sensor coupled with supervised ML can be a viable alternative means for monitoring the mosquito population.…”

Section: Related Workmentioning

confidence: 99%

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Predictive Supervised Machine Learning Models for Diabetes Mellitus

2020

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Diabetes mellitus (DM) is one of the deadliest diseases in the world, especially in developed nations. In recent years, it has become rampant in the developing nations such as Nigeria, posing more threats to individuals in the latter than those in the former. More than 415 million people were reported to suffer from DM worldwide as of 2015, with type 2 of the disease accounting for approximately 90% of the cases. The number of people with DM is expected to rise to 592 million by the year 2035. Therefore, DM is one of the growing public health concerns in Nigeria. In this study, the diagnostic dataset of DM type 2 was collected from the Murtala Mohammed Specialist Hospital, Kano, and used to develop predictive supervised machine learning models based on logistic regression, support vector machine, K-nearest neighbor, random forest, naive Bayes and gradient booting algorithms. The random forest predictive learning-based model appeared to be one of the best developed models with 88.76% in terms of accuracy; however, in terms of receiver operating characteristic curve, random forest and gradient booting predictive learning-based models were found to be the best predictive learning models with 86.28% predictive ability, respectively. Keywords Machine learning • Predictive model • Diabetes mellitus • Diabetes mellitus type 2 • Random forest This article is part of the topical collection "Advances in Computational Approaches for Artificial Intelligence, Image Processing, IoT and Cloud Applications" guest edited by Bhanu Prakash K N and M. Shivakumar".

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%