Telemedicine-equipped android interface-based heart rate monitoring

Fajrin, Hanifah Rahmi; Adi, Bayu Satrio; Purwoko, Heri; Sari, Irma Permata

doi:10.11591/ijeecs.v21.i3.pp1332-1340

Cited by 5 publications

(2 citation statements)

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“…These studies suggested that the mobile application can be developed and improved to diagnose hypertension. This point of view is supported by the studies conducted for a mobile application that are designed for hypertension and health management [43]- [45]. Besides, the computational process of the diagnosis can be placed in the cloud-based system to ensure data processing performance [46]- [50] and overcome the low computational memory in wearable devices [27].…”

Section: Discussionmentioning

confidence: 97%

Review on hypertension diagnosis using expert system and wearable devices

Sani¹,

Abdullah

Rosli

2022

IJECE

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<span>The popularity of smartphones and wearable devices is increasing in the global market. These devices track physical exercise records, heartbeat, medicines, and self-health diagnosis. The wearable devices can also collect personal health parameters include hypertension diagnosis. Hypertension is one of the risk factors for cardiovascular-related diseases among the Malaysian population. Many mobile applications are paired with wearable devices to monitor health conditions, but none of them able to diagnose hypertension. In this study, we reviewed research papers that focused on hypertension using expert systems and wearable devices. We performed a systematic literature review based on hypertension factors, expert systems, and wearable devices. We found 15 specific research papers after the filtering process. The key findings highlighted three main focuses, which are the factors of hypertension, the expert system techniques, and the types of sensors in wearable devices. Blood pressure is the most common factor of hypertension that can be collected by wearable devices. As for the expert system techniques, we determined the three most common techniques are machine learning, neural network, and fuzzy logic. Lastly, the wrist band is the most common sensor for wearable devices in hypertension-related research.</span>

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Section: Discussionmentioning

confidence: 97%

Review on hypertension diagnosis using expert system and wearable devices

Sani¹,

Abdullah

Rosli

2022

IJECE

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“…In any scenario, if critical information for treating and diagnosing patients was foreseen in advance, clinicians would have a lot simpler time collecting it. Heart illness is frequently misdiagnosed as coronary artery disease [2], [3]. The main contribution of the paper is as follows:  This research has more significant work for tracking and establishing numerous health monitoring apps.…”

Section: Introductionmentioning

confidence: 99%

Predictive analytics of heart disease presence with feature importance based on machine learning algorithms

Kolukula,

Pothineni,

Chinta

et al. 2023

IJEECS

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<span>Heart failure disease is a complex clinical issue which has more impact on life of human begins. Hospitals and cardiac centers frequently employ electrocardiogram (ECG) tool to assess and to identify heart failure at early stages. Healthcare professionals are very concerned about the early identification of heart disease. In this research paper we have focused on predictive analysis of cardiac disease by using machine learning algorithms. We have developed python-based software for healthcare research in this paper. This research has more significant work for tracking and establishing numerous health monitoring apps. We have demonstrated information handling that requires adjusting clear-cut portions and working with absolute factors. A quick overview of the various machine learning technologies based on heart disease diagnosis is described clearly in this research. A more reliable way for diagnosing cardiac problems is the random forest classification algorithm. This application needs data analysis, which is crucial owing to its about 95% accuracy rate across training data. We have discussed the tests and findings of the random forest classifier method, which improves the accuracy of heart disease research diagnosis.</span>

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