Novel Deep Learning Architecture for Predicting Heart Disease using CNN (original) (raw)
Related papers
Novel Deep Learning Architecture for Heart Disease Prediction using Convolutional Neural Network
2021
Healthcare is one of the most important aspects of human life. Heart disease is known to be one of the deadliest diseases which is hampering the lives of many people around the world. Heart disease must be detected early so the loss of lives can be prevented. The availability of large-scale data for medical diagnosis has helped developed complex machine learning and deep learning-based models for automated early diagnosis of heart diseases. The classical approaches have been limited in terms of not generalizing well to new data which have not been seen in the training set. This is indicated by a large gap in training and test accuracies. This paper proposes a novel deep learning architecture using a 1D convolutional neural network for classification between healthy and non-healthy persons to overcome the limitations of classical approaches. Various clinical parameters are used for assessing the risk profile in the patients which helps in early diagnosis. Various techniques are used ...
Heart Disease Prediction using CNN, Deep Learning Model
International Journal for Research in Applied Science & Engineering Technology, 2020
Heart disease is one of the most serious health threat growing among worldwide, for which mortality rate around the world is very high. Early detection of heart disease could save many lives, accurate detection of heart disease is crucial among the health care persons through regular clinical data and its analysis. Artificial intelligence is the effective solution for decision making and accurate heart disease predictions. Medical industry showing enormous development in using information technology, in which artificial intelligence play major role. In the proposed work, deep learning based approach on heart disease is done on Cleveland dataset. However existing studies are handled in Machine learning technique. The proposed work detects heart disease based in Convolutional Neural Networks. Experimental results shows our proposed work achieves high level of accuracy in prediction of heart disease.
A Deep Learning Method for Prediction of Cardiovascular Disease Using Convolutional Neural Network
Revue d'intelligence artificielle, 2020
Heart disease is a very deadly disease. Worldwide, the majority of people are suffering from this problem. Many Machine Learning (ML) approaches are not sufficient to forecast the disease caused by the virus. Therefore, there is a need for one system that predicts disease efficiently. The Deep Learning approach predicts the disease caused by the blocked heart. This paper proposes a Convolutional Neural Network (CNN) to predict the disease at an early stage. This paper focuses on a comparison between the traditional approaches such as Logistic Regression, K-Nearest Neighbors (KNN), Naï ve Bayes (NB), Support Vector Machine (SVM), Neural Networks (NN), and the proposed prediction model of CNN. The UCI machine learning repository dataset for experimentation and Cardiovascular Disease (CVD) predictions with 94% accuracy.
On Deep Neural Networks for Detecting Heart Disease
2018
Heart disease is the leading cause of death, and experts estimate that approximately half of all heart attacks and strokes occur in people who have not been flagged as "at risk." Thus, there is an urgent need to improve the accuracy of heart disease diagnosis. To this end, we investigate the potential of using data analysis, and in particular the design and use of deep neural networks (DNNs) for detecting heart disease based on routine clinical data. Our main contribution is the design, evaluation, and optimization of DNN architectures of increasing depth for heart disease diagnosis. This work led to the discovery of a novel five layer DNN architecture - named Heart Evaluation for Algorithmic Risk-reduction and Optimization Five (HEARO-5) -- that yields best prediction accuracy. HEARO-5's design employs regularization optimization and automatically deals with missing data and/or data outliers. To evaluate and tune the architectures we use k-way cross-validation as well...
Diagnosis of Cardiovascular Disease using Deep Learning
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023
Cardiovascular disease is one of the most horrendous illnesses, particularly the silent heart attack that strikes a person so abruptly that there is no time for treatment. It's difficult to diagnose a disease of this nature. One of the scariest diseases that c an kill a person at any time without warning is heart disease, and most doctors are unable to predict silent heart attacks. The lac k of specialists and an increase in cases of wrong diagnoses have fueled the demand for the creation of an efficient cardiovascul ar disease prediction system. This resulted in the exploration and development of original machine learning and medical data mi ning methodologies. The principal goal of this research is to identify the most crucial qualities for silent heart attack identificatio n by using classification algorithms to extract significant patterns and features from medical data. Although it is not innovative t o build such a system, the current ones have flaws and are not designed to detect the likelihood of silent heart attacks. Another is sue with the present heart attack prediction method is the use of characteristics. Choosing the typical features for the heart attac k prediction algorithm frequently yields unreliable results. To increase prediction accuracy, the suggested method aims to extract suitable attributes from the datasets. We developed a framework in this exploration that can understand the principles of predicti ng the risk profile of patients with the clinical data parameters. This research suggests an effective neural network with convolut ional layers to classify clinical data that is noticeably class-imbalanced. In order to forecast the development of Coronary Heart Disease, data from the National Health and Nutritional Examination Survey (NHANES) is collected (CHD). This research aime d to design a robust deep-learning algorithm to predict heart disease. Heart disease prediction is performed using SMOTE and MLP Classifier algorithms and Deep Neural Network Algorithms. The effectiveness of the model that accurately predicts the pre sence or absence of heart disease was examined using DNN and ANN. In this research article, we'll look at a machine-learnin g model that can clearly assess cardiac issues and be utilized by analysts and medical professionals.
Heart Disease Prediction Using CNN Algorithm
SN Computer Science, 2020
In this paper, we aim to predict accuracy, whether the individual is at risk of a heart disease. This prediction will be done by applying machine learning algorithms on training data that we provide. Once the person enters the information that is requested, the algorithm is applied and the result is generated. Obviously, the accuracy is expected to decrease when the medical data itself are incomplete. We implement the prediction model over real-life hospital data. We propose to use convolutional neural network algorithm as a disease risk prediction algorithm using structured and perhaps even on unstructured patient data. The accuracy obtained using the developed model ranges between 85 and 88%. We have proposed further by applying other machine learning algorithms over the training data to predict the risk of diseases, comparing their accuracies so that we can deduce the most accurate one. Attributes can also be modified in an attempt to improve the accuracy further.
Deep Learning Based Healthcare Method for Effective Heart Disease Prediction
EAI Endorsed Transactions on Pervasive Health and Technology
In many parts of the world, heart disease is the leading cause of mortality diagnosis is critical Towards Efficient Medical Care and prevention of heart attacks and other cardiac events. Deep learning algorithms have shown promise in accurately predicting heart disease based on medical data, including electrocardiograms (ECGs) and other health metrics. With this abstract, Specifically, we advocate for deep learning algorithm in accordance with CNNs for Deep Learning effective heart disease prediction. The proposed method uses a combination of ECG signals, demographic data, and clinical measurements Identifying risk factors for cardiovascular disease in patients. The proposed CNN-based model includes several layers, such as convolutional ones, pooling ones, and fully connected ones. The model takes input in the form of ECG signals, along with demographic data and clinical measurements, and uses convolutional layers to get features out of raw data. To lessen the effect of this, poolin...
ABUAD Journal of Engineering Research and Development, 2024
Heart disease is a leading cause of mortality globally and its prevalence is increasing year after year. Recent statistics from the World Health Organization show that about 17.9 million individuals are embattled with heart diseases annually and people under the age of 70 account for one-third of these deaths. Hence, there is need to intensify research on early heart disease prediction and artificial intelligence-based heart disease prediction systems. Previous heart disease prediction systems using machine learning techniques are unable to manage large amount of data, resulting in poor prediction accuracy. Hence, this research employs Convolutional Neural Networks, a deep learning approach for prediction of heart diseases. The dataset for training and testing the model was obtained from a government owned hospital in Nigeria and Kaggle. The resulting system was evaluated using precision, recall, f1-score and accuracy metrics. The results obtained are: 0.94, 0.95, 0.95 and 0.95 for precision, recall, f1-score and accuracy respectively. This show that the CNN-based model responded very well to the prediction of heart diseases for both negative and positive classes. The results obtained were also compared to some selected machine-learning models like Random Forest, Naïve Bayes, KNN and Logistic Regression and results show that the developed model achieved a significant improvement over the methods considered. Therefore, convolutional neural network is more suitable for heart disease prediction than some state-of-the-art machine-learning models. The contribution to knowledge of this research is the use of Afrocentric dataset for heart disease prediction. Future research should consider increasing the data size for model training to achieve improved accuracy.
Prediction of Heart Disease Using Machine Learning and Deep Learning Techniques
IRJET, 2023
The primary cause of death has historically been heart related disease worldwide over past few decades, thus it is crucial and worrisome to anticipate any such disorders. Heart-related disease diagnosis and prognosis is a difficult task that calls for greater accuracy, correctness, and perfection since a small error can result in weariness or even death, which has a significant global impact. Due to a multitude of risk factors, such as smoking, diabetes, high cholesterol, and similar conditions, it can be challenging to diagnose heart disease. As a result of these circumstances, it is urgent to develop precise, practical, and trustworthy methods for making an early diagnosis, as doing so will benefit people everywhere by enabling them to receive the necessary therapy before the condition worsens. The data from the dataset is obtained using contemporary methods like data mining and machine learning techniques, and the fetched data is then utilised to forecast cardiac disease. With the help of deep learning techniques like CNN and MLP as well as machine learning methods like ADABOOST and EXTRATREES, this work attempts to predict the likelihood of getting cardiac illnesses.
A Clinical Decision Support System for Heart Disease Prediction Using Deep Learning
IEEE Access
Unfortunately, heart disease is currently the primary cause of mortality worldwide and its incidence is increasing. Detecting heart disease in its initial stages before a cardiac event takes place poses challenges. Huge amount of heart disease data is available in the health care sector such as in clinics, hospitals etc. However, this data is not intelligently handled to identify the hidden patterns. Machine learning techniques help in turning this medical data into useful knowledge. Machine learning is used to design such decision support systems (DSS) that can learn and improve from their past experiences. Recently, deep learning has gained the interest of industry and academics. The fundamental objective of this research activity is the precise diagnosis of heart illness. The suggested approach makes use of a Kerasbased deep learning model to compute results with a dense neural network. The proposed model undergoes testing with various configurations of hidden layers in the dense neural network, ranging from 3 layers to 9 layers. Each hidden layer employs 100 neurons and utilizes the Relu activation function. To carry out the analysis, several heart disease datasets are utilized as benchmarks. The assessment encompasses both individual and ensemble models, and is performed on all heart disease datasets. Furthermore, using important measures like sensitivity, specificity, accuracy, and f-measure, the dense neural network is assessed across all datasets. The performance of different layer combinations varies across datasets due to varying attribute categories. Through extensive experimentation, the results of the proposed framework are analyzed. The study's conclusions show that, when applied to all heart disease datasets, the deep learning model suggested in this research paper achieves superior accuracy, sensitivity, and specificity compared to individual models and alternative ensemble approaches.