Approach to Heart Diseases Diagnosis and Monitoring through Machine Learning and iOS Mobile Application

Dharmasiri, N.D.K.G.; Vasanthapriyan, Shanmuganathan

doi:10.1109/icter.2018.8615504

Cited by 7 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…KNN get neighbors among data using Euclidean distance between points of data. This algorithm is used for classification and regression problems [28,29,30]. The value of k (where k is a constant defined by the user) will find all of the similar existing feature cases with the new case and surround all cases to find the new case for a similar category [31].…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

The Role of Machine Learning Algorithms for Diagnosing Diseases

Ibrahim

Abdulazeez

2021

JASTT

177

View full text Add to dashboard Cite

Nowadays, machine learning algorithms have become very important in the medical sector, especially for diagnosing disease from the medical database. Many companies using these techniques for the early prediction of diseases and enhance medical diagnostics. The motivation of this paper is to give an overview of the machine learning algorithms that are applied for the identification and prediction of many diseases such as Naïve Bayes, logistic regression, support vector machine, K-nearest neighbor, K-means clustering, decision tree, and random forest. In this work, many previous studies were reviewed that used machine learning algorithms for detecting various diseases in the medical area in the last three years. A comparison is provided concerning these algorithms, assessment processes, and the obtained results. Finally, a discussion of the previous works is presented.

show abstract

Section: Machine Learning Algorithmsmentioning

confidence: 99%

The Role of Machine Learning Algorithms for Diagnosing Diseases

Ibrahim

Abdulazeez

2021

JASTT

177

View full text Add to dashboard Cite

show abstract

“…Internet of Things (IoT) as a global network of smart devices that can sense and interact with their environment for communication with users and other things (smart devices) and systems has played critical roles in enhancing healthcare delivery [5]. With increased attention on efficiency and better outcomes, the diffusion of IoT and ML could play an immensely important role in reducing healthcare costs without reducing the quality of care delivered to patients [6]. Although there are innovative implications of the integration of IoT in modern health applications, remote patient monitoring system that integrates IoT with ML has received less attention.…”

Section: Introductionmentioning

confidence: 99%

“…However, IoT and ML have the potential to bring about new medical applications such as remote health monitoring, chronic disease, fitness programs, and adult care. Hence, medical devices including diagnostic and imaging devices and sensors can refer to smart devices representing the core of IoT [6].…”

Section: Introductionmentioning

confidence: 99%

Internet of Things based Framework for Smart Healthcare Using Hybrid Machine Learning

Okemiri

Uzoma

Christopher

2022

Preprint

View full text Add to dashboard Cite

The world has been facing the challenge of increased cardiovascular death rate for decades now. The utilization of machine learning techniques coupled with the power of Internet of Things can be an effective solution to this real world problem. This research presents Internet of Things based framework for smart healthcare using hybrid machine learning for monitoring heart disease, a system specifically designed for wearable devices that facilitates heart diseases monitoring. The system uses wearable sensors to collect observable vital signs which is contextualized with data from clinical database for a high efficiency in analysis and prediction. Internet of Things and Machine Learning have gained wide applicability in the healthcare sector. The problem of inaccurate Electrocardiogram interpretation and high dependency on individual interpretation, coupled with delayed analysis and diagnosis of patients and lack of accurate prognosis are what this research solves. Machine learning was used accurate analysis and predictions of diseases while the internet of things components was communicating the server for smartness. The framework consists of some components like data collection using wearable sensors, data analysis center, which uses hybridized machine learning algorithms, to quickly identify potential cardiovascular cases from real-time symptom data. This facilitates real-time communication between doctor and patient through wearable technologies. With this, real-time analysis and monitoring of cardiovascular disease would be possible. This system instead of transferring the raw data collected from patients directly to the health care professionals, sends those data to a python platform for analysis on the local device by feeding the hybrid of collected data to Random Forest, Naïve Bayes and Support Vector Machine (SVM) algorithms to analyze and monitor features extracted from clinical databases and wearable sensors and then classify a patient as “Negative” or “Positive” for heart disease. In this research, Object Oriented Methodology and Analysis was used with arduino and python programming languages. The results of our experiment showed that the system was successful in both analysis and classification of a patient’s heart disease with an accuracy of 87.6% for the hybrid machine learning. Random Forest model has a precision and recall that exceeded 100%, 75% precision and 65% recall for Naïve Bayes model and 77% precision and 56% recall for support vector machine. We used UCI clinical dataset of 1025 samples of patients with 25% for test data and 75% for the data training.

show abstract