scholarly journals Artificial Intelligence in Predicting Heart Failure

2021 ◽  
Author(s):  
Rashid Ebrahim Al-Mannai ◽  
Mohammed Hamad Almerekhi ◽  
Mohammed Abdulla Al-Mannai ◽  
Mishahira N ◽  
Kishor Kumar Sadasivuni ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Heart Failure ◽  
Health Care Systems ◽  
Critical Role ◽  
Failure Detection ◽  
Major Chronic Disease ◽  
Care Systems ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

Heart Failure is a major chronic disease that is increasing day by day and a great health burden in health care systems world wide. Artificial intelligence (AI) techniques such as machine learning (ML), deep learning (DL), and cognitive computer can play a critical role in the early detection and diagnosis of Heart Failure Detection, as well as outcome prediction and prognosis evaluation. The availability of large datasets from difference sources can be leveraged to build machine learning models that can empower clinicians by providing early warnings and insightful information on the underlying conditions of the patients

scholarly journals A Comprehensive Study of Artificial Intelligence and Machine Learning Approaches in Confronting the Coronavirus (COVID-19) Pandemic

2021 ◽  
pp. 002073142110174
Author(s):  
Md Mijanur Rahman ◽  
Fatema Khatun ◽  
Ashik Uzzaman ◽  
Sadia Islam Sami ◽  
Md Al-Amin Bhuiyan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Language Processing ◽  
Probabilistic Models ◽  
Health Care Systems ◽  
Learning Approaches ◽  
Care Systems ◽  
Novel Coronavirus ◽  
Comprehensive Study

The novel coronavirus disease (COVID-19) has spread over 219 countries of the globe as a pandemic, creating alarming impacts on health care, socioeconomic environments, and international relationships. The principal objective of the study is to provide the current technological aspects of artificial intelligence (AI) and other relevant technologies and their implications for confronting COVID-19 and preventing the pandemic’s dreadful effects. This article presents AI approaches that have significant contributions in the fields of health care, then highlights and categorizes their applications in confronting COVID-19, such as detection and diagnosis, data analysis and treatment procedures, research and drug development, social control and services, and the prediction of outbreaks. The study addresses the link between the technologies and the epidemics as well as the potential impacts of technology in health care with the introduction of machine learning and natural language processing tools. It is expected that this comprehensive study will support researchers in modeling health care systems and drive further studies in advanced technologies. Finally, we propose future directions in research and conclude that persuasive AI strategies, probabilistic models, and supervised learning are required to tackle future pandemic challenges.

scholarly journals A Review of Diabetes Mellitus Detection using Machine Learning Techniques

2021 ◽  
Vol 10 (6) ◽  
pp. 32-41
Author(s):  
R Kumar ◽  
Pazhanirajan S
Keyword(s):  
Artificial Intelligence ◽  
Diabetes Mellitus ◽  
Machine Learning ◽  
Machine Learning Techniques ◽  
Self Management ◽  
Self Administration ◽  
Comprehensive Overview ◽  
Learning Techniques ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

Diabetes Mellitus (DM) is a disease that can lead to a multi-organ malfunctioning in patients due to non-regulated diabetes. Recent advancements in machine learning (ML) and artificial intelligence, the early detection and diagnosis of DM is more advantageous than the manual diagnosis through an automated process. It this review, DM's recognition, diagnosis and self-management techniques from six facets, namely DM datasets, techniques involved in pre-processing, extraction of features; identification through ML; classification and diagnosis of DM; intelligent DM assistant based on artificial intelligence; are thoroughly analyzed and presented. The findings of the previous research and their inferences are interpreted. This analysis also offers a comprehensive overview of DM detection and self-administration technologies that can be of use to the research community working in the field of automated DM detection and self-management.

Biomarkers in acute heart failure

2015 ◽  
Author(s):  
Rajiv Choudhary ◽  
Kevin Shah ◽  
Alan Maisel
Keyword(s):  
Heart Failure ◽  
Acute Heart Failure ◽  
Natriuretic Peptides ◽  
Health Care Systems ◽  
Medical Problem ◽  
National Health Care ◽  
Neutrophil Gelatinase Associated Lipocalin ◽  
Acute Dyspnoea ◽  
Care Systems ◽  
Neutrophil Gelatinase

Acute heart failure continues to be a worldwide medical problem, associated with frequent readmissions, high mortality, and a profound economic impact on national health care systems. In the past decade, biomarkers have shifted the way in which acute heart failure is managed by the cardiologist. The search for the ideal biomarker to aid in the diagnosis, prognosis, and treatment of acute heart failure is ongoing. The natriuretic peptides have proved extremely useful in determining whether acute dyspnoea has a cardiac aetiology. In addition, recent trials have demonstrated the use of natriuretic peptides in inpatient and outpatient prognosis, as well as in titrating medications in outpatients with chronic heart failure to prevent acute heart failure hospitalizations. Other emerging acute heart failure biomarkers include mid-regional pro-adrenomedullin, mid-regional proatrial natriuretic peptide, troponin, ST2, and neutrophil gelatinase-associated lipocalin.

scholarly journals AN IN-DEPTH ANALYSIS OF THE IDENTIFIED ALGORITHMS AND THEIR COMPARATIVE STUDY IN THE EARLY DETECTION AND DIAGNOSIS OF BREAST CANCER

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Mridul Sharma
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Early Detection ◽  
Healthcare Workers ◽  
State Of The Art ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Depth Analysis ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

These days one of the major inevitable ailments for females is bosom malignancy. The appropriate medication and early findings are important stages to take to thwart this ailment. Although, it's not easy to recognize due to its few vulnerabilities and lack of data. Can use artificial intelligence to create devices that can help doctors and healthcare workers to early detection of this cancer. In This research, we investigate three specific machine learning algorithms widely used to detect bosom ailments in the breast region. These algorithms are Support vector machine (SVM), Bayesian Networks (BN) and Random Forest (RF). The output in this research is based on the State-of-the-art technique.

scholarly journals Prediction on Cardiovascular disease using Decision tree and Naïve Bayes classifiers

2022 ◽  
Vol 2161 (1) ◽  
pp. 012015
Author(s):  
V Sai Krishna Reddy ◽  
P Meghana ◽  
N V Subba Reddy ◽  
B Ashwath Rao
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Risk Factors ◽  
Heart Failure ◽  
Cardiovascular Disease ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Correct Decision ◽  
Medical Field

Abstract Machine Learning is an application of Artificial Intelligence where the method begins with observations on data. In the medical field, it is very important to make a correct decision within less time while treating a patient. Here ML techniques play a major role in predicting the disease by considering the vast amount of data that is produced by the healthcare field. In India, heart disease is the major cause of death. According to WHO, it can predict and prevent stroke by timely actions. In this paper, the study is useful to predict cardiovascular disease with better accuracy by applying ML techniques like Decision Tree and Naïve Bayes and also with the help of risk factors. The dataset that we considered is the Heart Failure Dataset which consists of 13 attributes. In the process of analyzing the performance of techniques, the collected data should be pre-processed. Later, it should follow by feature selection and reduction.

Biomarkers in acute heart failure

2018 ◽  
Author(s):  
Rajiv Choudhary ◽  
Kevin Shah ◽  
Alan Maisel
Keyword(s):  
Heart Failure ◽  
Acute Heart Failure ◽  
Health Care Systems ◽  
Medical Problem ◽  
National Health Care ◽  
Neutrophil Gelatinase Associated Lipocalin ◽  
Acute Dyspnoea ◽  
Care Systems ◽  
Neutrophil Gelatinase ◽  
The Ideal

Acute heart failure continues to be a worldwide medical problem, associated with frequent readmissions, high mortality, and a profound economic impact on national health care systems. In the past decade, biomarkers have shifted the way in which acute heart failure is managed by the cardiologist. The search for the ideal biomarker to aid in the diagnosis, prognosis, and treatment of acute heart failure is ongoing. The natriureticfc peptides have proved extremely useful in determining whether acute dyspnoea has a cardiac aetiology. In addition, recent trials have demonstrated the use of natriuretic peptides in inpatient and outpatient prognosis, as well as in titrating medications in outpatients with chronic heart failure to prevent acute heart failure hospitalizations. Other emerging acute heart failure biomarkers include mid-regional pro-adrenomedullin, mid-regional proatrial natriuretic peptide, troponin, ST2, and neutrophil gelatinase-associated lipocalin.

scholarly journals Usefulness of heart rhythm complexity in heart failure detection and diagnosis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Cheng-Hsuan Tsai ◽  
Hsi-Pin Ma ◽  
Yen-Tin Lin ◽  
Chi-Sheng Hung ◽  
Shan-Hsuan Huang ◽  
...  
Keyword(s):  
Heart Failure ◽  
Complexity Analysis ◽  
Heart Rhythm ◽  
Failure Detection ◽  
Left Ventricular ◽  
Multiscale Entropy ◽  
Left Ventricular Ejection ◽  
Fluctuation Analysis ◽  
Ventricular Ejection Fraction ◽  
Detection And Diagnosis

Abstract Heart failure (HF) is a major cardiovascular disease worldwide, and the early detection and diagnosis remain challenges. Recently, heart rhythm complexity analysis, derived from non-linear heart rate variability (HRV) analysis, has been proposed as a non-invasive method to detect diseases and predict outcomes. In this study, we aimed to investigate the diagnostic value of heart rhythm complexity in HF patients. We prospectively analyzed 55 patients with symptomatic HF with impaired left ventricular ejection fraction and 97 participants without HF symptoms and normal LVEF as controls. Traditional linear HRV parameters and heart rhythm complexity including detrended fluctuation analysis (DFA) and multiscale entropy (MSE) were analyzed. The traditional linear HRV, MSE parameters and DFAα1 were significantly lower in HF patients compared with controls. In regression analysis, DFAα1 and MSE scale 5 remained significant predictors after adjusting for multiple clinical variables. Among all HRV parameters, MSE scale 5 had the greatest power to differentiate the HF patients from the controls in receiver operating characteristic curve analysis (area under the curve: 0.844). In conclusion, heart rhythm complexity appears to be a promising tool for the detection and diagnosis of HF.

Biomarkers in acute heart failure

2021 ◽  
pp. 409-424
Author(s):  
Rajiv Choudhary ◽  
Nicholas Wettersten ◽  
Kevin Shah ◽  
Alan Maisel
Keyword(s):  
Heart Failure ◽  
Health Care ◽  
Acute Heart Failure ◽  
Health Care Systems ◽  
High Sensitivity ◽  
Medical Problem ◽  
National Health Care ◽  
Acute Dyspnoea ◽  
Galectin 3 ◽  
Care Systems

Acute heart failure continues to be a worldwide medical problem, associated with frequent readmissions, high mortality, and a profound economic impact on national health care systems. Biomarkers have shifted the way in which acute heart failure is managed by the cardiologist. The search for the ideal biomarker to aid in the diagnosis, prognosis, and treatment of acute heart failure is ongoing. The natriuretic peptides have come close to an ideal biomarker and prove extremely useful in determining whether acute dyspnoea has a cardiac aetiology. Furthermore, they are useful for prognosis and may have a role in guiding management of heart failure. Additionally, high-sensitivity troponin, sST2, and galectin-3 are increasingly used with mounting clinical evidence for utility. Other emerging acute heart failure biomarkers include mid-regional pro-adrenomedullin, bio-adrenomedulin, mid-regional proatrial natriuretic peptide, and procalcitonin.

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