Human Heart Disease Prediction System Using Data Mining Techniques

Javed, Sidra; Javed, Hamza; Saddique, Ayesha; Rafiq, Beenish

doi:10.33317/ssurj.v8iii.92

Cited by 3 publications

(2 citation statements)

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“…Many research groups have made significant contributions in this area, and some of their approaches are highlighted here. J. Thomas et al [1] utilized data mining techniques and applied K-nearest neighbor, neural network, naive Bayes, and decision tree algorithms to predict heart disease risk rates. M. Ashu Sharma et al focused on cleaning and preprocessing the dataset and found that neural networks yielded high precision in heart disease prediction [2].…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization Heart Disease Prediction using Machine Learning Models

Tuba

2023

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Healthcare is currently one of the most pressing global issues, with an increase in the incidence of cardiac disease affecting all age groups, particularly the young. Rapid identification and treatment of heart problems can potentially save lives. Artificial intelligence has the potential to significantly aid in this effort. In this study, we aimed to develop a heart disease prediction model using machine learning techniques. We utilized several models, including Support Vector Machine (SVM), K-Neighbors Classifier, Random Forest Classifier, Decision Tree, and Logistic Regression. Based on our experiments, the logistic regression and K-NN models produced the best results, with accuracies of 0.95592% and 0.956194%, respectively. Our findings suggest that machine learning models can be optimized for heart disease prediction and have the potential to improve healthcare outcomes.

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Section: Literature Reviewmentioning

confidence: 99%

Optimization Heart Disease Prediction using Machine Learning Models

Tuba

2023

Fidelity

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“…The developed ECG monitoring device modules on PCB and attachable electrodes are shown in Figure 9 weighing a total of 580 grams. It is to note that the transformer weight alone weighs 350 grams, which can be replaced with a wave rectifier and a chopper circuit [20][21][22][23][24][25][26].…”

Section: G Serial To Usb Communicationmentioning

confidence: 99%

Low-Cost Portable ECG Monitoring Device for Inaccessible Areas in Pakistan

Farooq

Aroos

Mumtaz

et al. 2022

SSURJET

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A need for constant health monitoring system is essential especially for heart diseases which may cause sudden stroke and death. We present in this paper the design and development of a low cost, efficient and user-friendly ECG monitoring device. Most of the heart rate measurement devices and tools available are quite expensive and may not be easily available in some areas. Presented device monitors the patient’s body information using three lead silver chloride ECG disposable electrodes and detects the pulse signal. The detected heart pulse signal is first filtered and then amplified. The device displays the heart signal on a MATLAB graphical user interface while simultaneously showing the digitized pulse rate on a digital LCD. The device was tested on female patients aged 20-24. The results obtained were reliable compared with electrocardiogram reports and similar available devices of much higher development cost.

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Assistive Device for Rehabilitation of Acute-Stroke Upper Extremity

Sattar¹

2021

SSURJET

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Stroke is a medical condition that is the fifth cause of death and a leading cause of disability. The different reasons for stroke are high B.P, obesity, smoking, etc. recovery can only be sped up by regular physiotherapy and willpower of the patient. Regular physiotherapy can be interrupted by other commitments of the patient or by the doctor's unavailability. This may lead to a delay in the recovery process. Therefore, the efforts are needed to tackle the problem. The proposed device can be used to monitor and carry out selected exercises advised by the doctor. Here the upper right limb is our focus because it is the most common area to be affected. Furthermore, android application is also designed which have different consoles for doctor and patient. The patient’s app shows the history and progress of the patient while the doctor sets the time and can also see the progress. Firstly, the unit tracks finger and wrist movements and the signal strength of the muscles being exercised, and then performs assisted flexion / extension manipulation of the limb. The results are shown in the app using the graphs so that the patient and the doctor can both monitor the progress. Thus, this design gives ease to the patient as well as the doctor. With the world shifting towards smart devices and remote monitoring and control, there is a need to apply modern technology in the health dept. using the platform of IOT, certain medical treatments can be made easier for the doctors and effective for the patients. By using different sensors, a microcontroller, Arduino software, mobile application, the patient’s physiotherapy exercises can be monitored, recorded and assisted.

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Human Heart Disease Prediction System Using Data Mining Techniques

Cited by 3 publications

References 0 publications

Optimization Heart Disease Prediction using Machine Learning Models

Optimization Heart Disease Prediction using Machine Learning Models

Low-Cost Portable ECG Monitoring Device for Inaccessible Areas in Pakistan

Assistive Device for Rehabilitation of Acute-Stroke Upper Extremity

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