Machine Learning-Based Heart Patient Scanning, Visualization, and Monitoring

Ahdal, Ahmed Al; Prashar, Deepak; Rakhra, Manik; Wadhawan, Ankita

doi:10.1109/iccs54944.2021.00049

Cited by 10 publications

(3 citation statements)

References 5 publications

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“…The logistic regression technique is considered one of the most suitable numerical models for estimating the probability of a particular class or event, such as success or failure [ 17 ]. The logistical regression employs numerous anticipated variables, which might be digital or class-based.…”

Section: Methodsmentioning

confidence: 99%

“…A massive data report and clinical diagnosis of the patient's cure and treatment are extremely difficult to set up in an accurate way; otherwise, these will be affected due to insufficient storage or management. This amount of data needs special means or tools to extract and process efficiently, by using one of the machine learning applications such as a classifier which can divide the data according to their attributes; this can be used in medical data analysis or disease detection [ 17 ]. ML was initially designed to observe medical data sets.…”

Section: Introductionmentioning

confidence: 99%

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[Retracted] Monitoring Cardiovascular Problems in Heart Patients Using Machine Learning

Ahdal

Rakhra

Arslan

et al. 2023

Journal of Healthcare Engineering

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The World Health Organization reports that heart disease is the most common cause of death globally, accounting for 17.9 million fatalities annually. The fundamentals of a cure, it is thought, are important symptoms and recognition of the illness. Traditional techniques are facing many challenges, ranging from delayed or unnecessary treatment to incorrect diagnoses, which can affect treatment progress, increase the bill, and give the disease more time to spread and harm the patient’s body. Such errors could be avoided and minimized by employing ML and AI techniques. Many significant efforts have been made in recent years to increase computer-aided diagnosis and detection applications, which is a rapidly growing area of research. Machine learning algorithms are especially important in CAD, which is used to detect patterns in medical data sources and make nontrivial predictions to assist doctors and clinicians in making timely decisions. This study aims to develop multiple methods for machine learning using the UCI set of data based on individuals’ medical attributes to aid in the early detection of cardiovascular disease. Various machine learning techniques are used to evaluate and review the results of the UCI machine learning heart disease dataset. The proposed algorithms had the highest accuracy, with the random forest classifier achieving 96.72% and the extreme gradient boost achieving 95.08%. This will assist the doctor in taking appropriate actions. The proposed technology will only be able to determine whether or not a person has a heart issue. The severity of heart disease cannot be determined using this method.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

[Retracted] Monitoring Cardiovascular Problems in Heart Patients Using Machine Learning

Ahdal

Rakhra

Arslan

et al. 2023

Journal of Healthcare Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is not even considering the case of India and it is true while considering the whole world. The Table 1 shows the accuracy of various Machine Learning Algorithms used for diagnosis of heart diseases [1]. The goal of the SVM algorithm is to create the best line or decision boundary that can segregate ndimensional space into classes, so that we can easily put the new data point in the correct category in the future.…”

Section: Introductionmentioning

confidence: 99%

A Review on Machine Learning-Based Algorithms for Heart Disease Diagnosis and Prediction

Ali

Manikandan

2022

IJSRCSEIT

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This work concentrates on different machine learning techniques that can be applied for the early diagnosis of heart diseases. Different Machine Learning Algorithms like KNN (K-Nearest Neighbour), Decision tree, Logistic Regression, Support Vector Machine, Random Forest and Naïve Bayes are used for prediction and detecting heart disease. The main purpose of this survey is to pay our attention towards machine learning techniques for diagnosing heart diseases in an efficient and effective manner.

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Machine Learning Techniques to Detect Heart Disease in Early Stage Using Feature-Based Classification

Rajak,

Kumar,

Panda

et al. 2023

Algorithms for Intelligent Systems

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Machine Learning-Based Heart Patient Scanning, Visualization, and Monitoring

Cited by 10 publications

References 5 publications

[Retracted] Monitoring Cardiovascular Problems in Heart Patients Using Machine Learning

[Retracted] Monitoring Cardiovascular Problems in Heart Patients Using Machine Learning

A Review on Machine Learning-Based Algorithms for Heart Disease Diagnosis and Prediction

Machine Learning Techniques to Detect Heart Disease in Early Stage Using Feature-Based Classification

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