Machine learning approach for diagnosis of heart diseases

M Makram, N Ali, A Mohammed - 2022 2nd International Mobile …, 2022 - ieeexplore.ieee.org
For decades, cardiovascular diseases have been the leading cause of death. According to
the most recent WHO data, coronary heart disease deaths in Egypt accounted for 29.38 …

An exploration of new methods for metabolic syndrome examination by infrared thermography and knowledge mining

BH Mi, WZ Zhang, YH Xiao, WX Hong, JL Song… - Scientific Reports, 2022 - nature.com
Metabolic syndrome (MS) is a clinical syndrome with multiple metabolic disorders. As the
diagnostic criteria for MS still lacking of imaging laboratory method, this study aimed to …

[图书][B] Adaptive Health Management Information Systems: Concepts, Cases, and Practical Applications: Concepts, Cases, and Practical Applications

J Tan - 2019 - books.google.com
Adaptive Health Management Information Systems, Fourth Edition is a thorough resource for
a broad range of healthcare professionals–from informaticians, physicians and nurses, to …

Risk prediction of ischemic heart disease using artificial neural network

M Raihan, PK Mandal, MM Islam… - 2019 international …, 2019 - ieeexplore.ieee.org
The fatty plaque deposits narrow artery walls leading to the heart Ischemic Heart Disease
(IHD). For that, the flowing of blood is reduced. Hyperpiesia polygenic disorder, drug …

Artificial intelligence approaches to physiological parameter analysis in the monitoring and treatment of non-communicable diseases: A review

JA Ramirez-Bautista, SL Chaparro-Cárdenas… - … Signal Processing and …, 2024 - Elsevier
The wide availability of electronic medical data from multiple sources, such as clinical
settings and wearable devices, evidences an important opportunity for the analysis of non …

Performance-enhanced KNN algorithm-based heart disease prediction with the help of optimum parameters

H Takci - Journal of the Faculty of Engineering and …, 2022 - avesis.cumhuriyet.edu.tr
Heart diseases are diseases with a high mortality rate. Clinical methods and machine
learning methods have been used frequently in the diagnosis of the disease. In this study …

Swarm Intelligence Algorithms-Based Machine Learning Framework for Medical Diagnosis: A Comprehensive Review

EH Houssein, E Saber, YM Wazery, AA Ali - Integrating meta-heuristics …, 2022 - Springer
When building medical diagnosis software, one of the most difficult challenges in disease
prediction. Machine Learning (ML) approaches have proven to be effective in a range of …

Subpopulation-specific machine learning prognosis for underrepresented patients with double prioritized bias correction

S Afrose, W Song, CB Nemeroff, C Lu… - Communications medicine, 2022 - nature.com
Background Many clinical datasets are intrinsically imbalanced, dominated by
overwhelming majority groups. Off-the-shelf machine learning models that optimize the …

Integrated k-means clustering with nature inspired optimization algorithm for the prediction of disease on high dimensional data

A Alam, M Muqeem - 2022 international conference on …, 2022 - ieeexplore.ieee.org
As per WHO report, 86.6% of deaths in China is due to chronic disease. Also, on average,
12 million people dies every year due to heart attack. On another side, we have a …

An improved hybrid model for cardiovascular disease detection using machine learning in IoT

A Naseer, MM Khan, F Arif, W Iqbal, A Ahmad… - Expert …, 2023 - Wiley Online Library
Cardiovascular disease (CVD) believes to be a major cause of transience and indisposition
worldwide. Early diagnosis and timely intervention are critical in preventing the progression …