scholarly journals Stratification of Cardiovascular Diseases Using Deep Learning

2020 ◽  
Vol 34 (4) ◽  
pp. 377-385
Author(s):  
Bhanu Prakash Doppala ◽  
Debnath Bhattacharyya ◽  
Midhun Chakkravarthy
Keyword(s):  
Deep Learning ◽  
Cardiovascular Diseases ◽  
Magnetic Resonance ◽  
Hybrid Algorithm ◽  
Heart Diseases ◽  
Neural Model ◽  
Ant Colony ◽  
World Health ◽  
African Buffalo ◽  
Fuzzy Neural

Heart-based diseases are one of the causes for major death rate in the world. WHO (World Health Organization) specified that 17 million of people are losing their lives per year due to several heart diseases. Artificial Intelligence playing a prominent role in disease identification and prediction from medical data. Magnetic Resonance Imaging plays a vital role in producing detailed images of internal organs and soft tissues for better understanding the condition. Magnetic Resonance Image contains more noisy data this is one of the issues to be addressed, hence this research focuses on the prediction of cardiovascular diseases using an innovative hybrid algorithm and addresses the issue of noise using Hann filters. A Hybrid algorithm is proposed with combination of Cat Fuzzy Neural Model (CFuNM) and Hybrid Ant Colony and African Buffalo Optimization. Cat Fuzzy Neural Model (CFuNM) is used to classify cardiac diseases such as cardiomyopathy, pericardial effusion, coronary artery, amyloidosis, and other coronary heart diseases and for the severity analysis of disease we used Hybrid Ant Colony and African Buffalo Optimization (HAC-ABO) mechanism. This research of Hybrid deep learning model improved the classification accuracy of 99.3% and error rate of 0.18% which is considerably good when compared to existing methods.

scholarly journals Cardiovascular Disease Prediction using Data Mining Techniques

2017 ◽  
Vol 10 (2) ◽  
pp. 520-528 ◽  
Author(s):  
Mudasir Kirmani
Keyword(s):  
Data Mining ◽  
Cardiovascular Disease ◽  
Cardiovascular Diseases ◽  
High Risk ◽  
Learning Algorithm ◽  
Heart Diseases ◽  
Circulatory System ◽  
World Health ◽  
Support Vector ◽  
Predictive Tool

Cardiovascular disease represents various diseases associated with heart, lymphatic system and circulatory system of human body. World Health Organisation (WHO) has reported that cardiovascular diseases have high mortality rate and high risk to cause various disabilities. Most prevalent causes for cardiovascular diseases are behavioural and food habits like tobacco intake, unhealthy diet and obesity, physical inactivity, ageing and addiction to drugs and alcohol are to name few. Factors such as hypertension, diabetes, hyperlipidemia, Stress and other ailments are at high risk to cardiovascular diseases. There have been different techniques to predict the prevalence of cardiovascular diseases in general and heart disease in particular from time to time by implementing variety of algorithms. Detection and management of cardiovascular diseases can be achieved by using computer based predictive tool in data mining. By implementing data mining based techniques there is scope for better and reliable prediction and diagnosis of heart diseases. In this study we studied various available techniques like decision Tree and its variants, Naive Bayes, Neural Networks, Support Vector Machine, Fuzzy Rules, Genetic Algorithms, and Ant Colony Optimization to name few. The observations illustrated that it is difficult to name a single machine learning algorithm for the diagnosis and prognosis of CVD. The study further contemplates on the behaviour, selection and number of factors required for efficient prediction.

scholarly journals Association Between Dietary Diversity, Waist to Hip Ratio and Cardiovascular Diseases Among Selected African Women (P10-079-19)

2019 ◽  
Vol 3 (Supplement_1) ◽  
Author(s):  
Sunday NUPO ◽  
Clara Oguntona ◽  
Babatunde Oguntona ◽  
Abosede Nupo ◽  
Jokodola Akinlotan ◽  
...  
Keyword(s):  
Cardiovascular Diseases ◽  
Dietary Diversity ◽  
Disease Risk ◽  
Heart Diseases ◽  
Cardiovascular Disease Risk ◽  
Meat Products ◽  
Density Lipoprotein ◽  
World Health ◽  
Average Risk ◽  
Waist To Hip Ratio

Abstract Objectives This study investigated the association between the dietary diversity, waist to hip ratio and cardiovascular diseases among women African. Methods A longitudinal study was carried out among randomly selected one thousand eight hundred and ninety eight ready and willing women in Nigeria. A pretested structured questionnaire was used to elicit information on socio-demographic characteristics and physical activity pattern of the respondents. Information on dietary diversity score (DDS) was obtained using a standardized Food and Nutrition Technical Assistant (FANTA) Project Questionnaire. Body Mass Index (BMI), Waist to Hip Ratio (WHR) and Mid Upper Arm Circumference (MUAC) were calculated from anthropometric measurements and used to classify subjects’ nutritional status. Nutrient intake was obtained using 24-hour dietary recall technique. The blood pressures of the subjects were measured using sphygmomanometer and classified using World Health Organization standard. The total cholesterol (TC), triglycerides (TG), low density lipoprotein (LDL), high density lipoprotein (HDL) and fasting blood sugar (FBS) were determined from collected blood samples of selected participants. Cardiovascular Risk was determined using American Heart Diseases software Version10. Data collected were analyzed using Statistical package for social science version 21. Results The DDS for cereals and grain (1.5 ± 0.2), Seeds, nuts and legumes (0.41 ± .4), starchy, roots and tubers (1.8 ± 5 and 1.59 ± 0.5), Fruits group (0.53 ± 0.19), meat and meat products (0.36 ± 0.4), fish and sea foods (1.09 ± 0.3), Oil and dairy group (0.17 ± 0.4).The BMI showed that 19% had obesity grade I, 8% had obesity grade II while WHR indicated that 6% were overweight and 10% obese. The mean energy intake was 2068 ± 957 kcal while the protein intake was 116 ± 59 g/day. Desirable TC level (<200 mg/dl) was found in 80% of the selected subjects while the normal TG (<150 mg/dl) and LDL (<129 mg/dl) was found in 95% and 90% subjects respectively. Cardiovascular disease risk showed that (93%) of the subjects had low risk while 7% had average risk. Conclusions The study showed significant relationship (r < 0.05) between dietary diversity, waist to hip ratio, obesity as well as sedentary lifestyle and the risk of developing cardiovascular diseases. Funding Sources TETFUND NIGERIA.

scholarly journals Deep learning-based ambient assisted living for self-management of cardiovascular conditions

2021 ◽  
Author(s):  
Maria Ahmed Qureshi ◽  
Kashif Naseer Qureshi ◽  
Gwanggil Jeon ◽  
Francesco Piccialli
Keyword(s):  
Deep Learning ◽  
Cardiovascular Diseases ◽  
Learning Strategies ◽  
Assisted Living ◽  
New Technologies ◽  
Ambient Assisted Living ◽  
World Health ◽  
Self Management ◽  
Ageing Population ◽  
Cloud Models

AbstractAccording to the World Health Organization, cardiovascular diseases contribute to 17.7 million deaths per year and are rising with a growing ageing population. In order to handle these challenges, the evolved countries are now evolving workable solutions based on new communication technologies such as ambient assisted living. In these solutions, the most well-known solutions are wearable devices for patient monitoring, telemedicine and mHealth systems. This systematic literature review presents the detailed literature on ambient assisted living solutions and helps to understand how ambient assisted living helps and motivates patients with cardiovascular diseases for self-management to reduce associated morbidity and mortalities. Preferred reporting items for systematic reviews and meta-analyses technique are used to answer the research questions. The paper is divided into four main themes, including self-monitoring wearable systems, ambient assisted living in aged populations, clinician management systems and deep learning-based systems for cardiovascular diagnosis. For each theme, a detailed investigation shows (1) how these new technologies are nowadays integrated into diagnostic systems and (2) how new technologies like IoT sensors, cloud models, machine and deep learning strategies can be used to improve the medical services. This study helps to identify the strengths and weaknesses of novel ambient assisted living environments for medical applications. Besides, this review assists in reducing the dependence on caregivers and the healthcare systems.

scholarly journals Machine learning-based identification of patients with a cardiovascular defect

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Nabaouia Louridi ◽  
Samira Douzi ◽  
Bouabid El Ouahidi
Keyword(s):  
Machine Learning ◽  
Cardiovascular Diseases ◽  
Heart Diseases ◽  
Heart Defects ◽  
Imbalanced Data ◽  
Machine Learning Techniques ◽  
World Health ◽  
Medical Problems ◽  
Multiple Data ◽  
Long Time

AbstractCardiovascular diseases had been for a long time one of the essential medical problems. As indicated by the World Health Association, heart ailments are at the highest point of ten leading reasons for death. Correct and early identification is a vital step in rehabilitation and treatment. To diagnose heart defects, it would be necessary to implement a system able to predict the existence of heart diseases. In the current article, our main motivation is to develop an effective intelligent medical system based on machine learning techniques, to aid in identifying a patient’s heart condition and guide a doctor in making an accurate diagnosis of whether or not a patient has cardiovascular diseases. Using multiple data processing techniques, we address the problem of missing data as well as the problem of imbalanced data in the publicly available UCI Heart Disease dataset and the Framingham dataset. Furthermore, we use machine learning to select the most effective algorithm for predicting cardiovascular diseases. Different metrics, such as accuracy, sensitivity, F-measure, and precision, were used to test our system, demonstrating that the proposed approach significantly outperforms other models.

A new medical Internet system in healthcare: innovation of Telegeriatrics and healthcare demands of older patients of different age groups (Preprint)

2020 ◽  
Author(s):  
Yu Gong ◽  
Jianyuan Zhou
Keyword(s):  
Cardiovascular Diseases ◽  
General Practitioners ◽  
Older Patients ◽  
Metabolic Diseases ◽  
Treatment Plan ◽  
Age Groups ◽  
The Elderly ◽  
Diagnosis And Treatment ◽  
World Health ◽  
New Model

BACKGROUND Healthcare for older patients is a worldwide challenge for public health system. A new medical Internet system in healthcare which is a new model of telegeriatrics system has been established. The key innovation is the new telegeriatrics system was conducted jointly by general practitioners in the Community Health Service Center and specialists in university teaching hospital. Unlike the typical telemedicine that has been practiced in other countries, the new model provides a solution for the key issues in telemedicine where a doctor is unable to conduct a direct physical examination and the associated potential diagnostic error. OBJECTIVE This study is to introduce the operation mechanism of the new Telegeriatrics system and analyze healthcare demands of older patients in different age groups applying the new Telegeriatrics system. METHODS 472 older patients (aged≥60) were enrolled and divided into the young older group (aged 60 to 74), the old older group (aged 75 to 89) and the very old group (aged≥90) according to the age stratification of World Health Organization. Proportion of the top 10 diseases of older patients of different age groups was analyzed. RESULTS The process of older patients’ diagnosis and treatment made by specialist and general practitioners formed a closed loop. It ensures the timeliness and effectiveness of diagnosis and treatment of older patients. The treatment effect can be observed by general practitioners and specialist can adjust the treatment plan in time. In this study, it was found that older patients in different age groups have different healthcare demands. Coronary heart disease and type 2 diabetes mellitus were found to be the main diseases of the older patients and the young older patients as well as the old older patients applying Telegeriatrics. CONCLUSIONS The new telegeriatrics system can provide convenient and efficient healthcare services for older patients and overcome the disadvantage of currently used models of telegeriatrics. Older patients in different age groups have different medical care demands. Cardiovascular diseases and metabolic diseases have become the main diseases of the elderly applying the new Telegeriatrics system. Healthcare policy makers should invest more medical resources to the prevention of cardiovascular diseases and metabolic diseases in the elderly.

scholarly journals Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Manan Binth Taj Noor ◽  
Nusrat Zerin Zenia ◽  
M Shamim Kaiser ◽  
Shamim Al Mamun ◽  
Mufti Mahmud
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Neurological Disorders ◽  
Magnetic Resonance Images ◽  
Future Research ◽  
Comparative Performance

Abstract Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.

Deep learning in magnetic resonance image reconstruction

2021 ◽  
Author(s):  
Shekhar S Chandra ◽  
Marlon Bran Lorenzana ◽  
Xinwen Liu ◽  
Siyu Liu ◽  
Steffen Bollmann ◽  
...  
Keyword(s):  
Deep Learning ◽  
Magnetic Resonance ◽  
Image Reconstruction ◽  
Magnetic Resonance Image
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