Heart Disease Prediction System Research Articles

Hybrid approach for Recurrence of Cardiovascular Disease Prediction Using Machine Learning Techniques

Cardiovascular disease remains a global health concern, being a leading cause of mortality. In response to the critical nature of heart-related conditions, this research focuses on the development of smart systems leveraging machine learning algorithms for accurate and timely diagnosis. The study explores various machine learning approaches to predict recurrence of heart diseases based on patient data encompassing key health factors. With heart disease being a prevalent cause of death worldwide, real-time forecasting methods from medical data sources have become crucial. The implementation of machine learning in healthcare demonstrates its potential for early and precise recurrence of disease detection. Despite the abundance of health information generated by medical institutions, there is an underutilization of this data, leading to a healthcare system that is "data rich" but "knowledge poor." This work aims to address this gap by presenting a reliable recurrence heart disease prediction system. The research highlights the necessity for effective analysis methods to uncover connections and patterns within healthcare data. The proposed prediction system utilizes a diverse set of health factors, contributing to a comprehensive understanding of heart disease. By leveraging machine learning algorithms, the system aims to enhance the accuracy and efficiency of diagnosis. The findings of this research not only contribute to the advancement of predictive healthcare but also underscore the significance of unlocking valuable insights from the vast pool of medical data. Ultimately, the implementation of such intelligent systems holds promise for improving patient outcomes and reducing the burden of cardiovascular diseases on global healthcare systems. The paper demonstrated Hybrid methods: Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes (NB), to build the prediction models. Data preprocessing and feature selection steps were done before building the models. The models were evaluated based on the accuracy, precision, recall, and F1-score. The Random Forest model performed best with 94.27% accuracy.

Hybrid Deep Learning and Squirrel Search Algorithm for Early Prediction of Heart Disease with IoT-Integrated Health Monitoring Systems

This research proposes a hybrid approach combining deep learning models with the Squirrel Search Algorithm (SSA) for early heart disease prediction. Integrating Internet of Things (IoT) devices for real-time health monitoring enhances predictive capability and provides continuous patient data. The proposed model optimizes feature selection using SSA, ensuring that the most relevant and impactful features are used for prediction, which improves the accuracy and efficiency of the deep learning models. This study aims to provide a robust, real-time heart disease prediction system that can be seamlessly integrated into modern healthcare infrastructures.

A Prediction of Heart Disease using IoT based ThingSpeak Basis and Deep Learning Method

Developing an IoT-based system with enhanced deep learning to predict heart disease aims to revolutionize healthcare. The system, by continuously monitoring vital signs, utilizes advanced machine learning for early detection of potential heart issues, emphasizing innovation for timely interventions and potentially life-saving outcomes. Select appropriate sensors for monitoring vital signs such as heart rate, blood pressure, ECG (Electrocardiogram), oxygen saturation, and temperature. Connect these sensors to an IoT platform using wireless communication protocols like Wi-Fi. Predicting heart disease using an IoT-based ThingSpeak framework is a practical and efficient way to monitor and predict heart-related issues in real-time. ThingSpeak is an IoT platform that allows you to collect, store, analyse, and visualize data from IoT sensors. Here's a step-by-step guide on how to develop a heart disease prediction system using ThingSpeak: Deploy the trained deep learning model to make real-time predictions based on the IoT sensor data. Implement strong security regulations. The input dataset is pre-processed approach for resolving error data and missing values. On ThingSpeak, we can apply preprocessing functions to your data. This might include data smoothing, outlier removal, or missing data handling. Pre-processed data values features are then classified into normal and abnormal by Convolution Neural Network-CNN. A 10-fold cross-validation tactic was employed during the model development procedure. In the experimental result comparisons of MCC calculation, the MD2N model attained the 0.86201 and DCNN perfect reached as 0.84111 and then DBN reached as 0.91157 and then AE model rate as 0.88662 and then proposed model reaches as 0.93291 correspondingly.

Optimization of resources in intelligent electronic health systems based on internet of things to predict heart diseases via artificial neural network

As a paradigm shift in tandem with the expansion of ICT, smart electronic health systems hold great promise for enhancing healthcare delivery and illness prevention efforts. These systems acquire an in-depth understanding of patient health states through the real-time collection and analysis of medical data enabled by the Internet of Things (IoT) and machine learning. With the widespread use of cutting-edge artificial intelligence and machine learning techniques, predictive analytics in medicine can assist in making the shift from a reactive to a proactive healthcare strategy. With the ability to rapidly and precisely evaluate massive amounts of data, draw intelligent conclusions, and solve difficult issues, artificial neural networks could revolutionize several industries. Two cardiac illnesses were assessed in this study using a multilayer perceptron artificial neural network that incorporated a genetic algorithm and an error-back propagation mechanism. The ability of artificial neural networks to handle consecutive time series data is crucial for optimizing resources in smart electronic health systems, especially with the increasing volume of patient information and the broad use of electronic clinical records. This requires the creation of more accurate predictive models. Through the use of Internet of Things (IoT) sensors, the proposed system gathers data, which is then used to do predictive analytics on patient history-related electronic clinical data saved in the cloud. A smart healthcare system that uses Mu-LTM (multidirectional long-term memory) to accurately monitor and predict the risk of heart disease has a coverage error of 97.94 %, an accuracy of 97.89 %, a sensitivity of 97.96 %, and a specificity of 97.99 %. In comparison to other smart heart disease prediction systems, the F1-score of 97.95 % and precision of 97.71 % is very good.

Development and Performance Evaluation of a Heart Disease Prediction Model Using Convolutional Neural Network

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.

Secure heart disease prediction model using ESVO-based Swish Bessel CNN classifier

Heart disease is a critical issue that affects people, causes serious sickness, and is the main cause of mortality worldwide. Early diagnosis of disease plays a significant role in heart disease prediction and is attained by various automation techniques. The availability of automation techniques initiates the necessity for medical data and the storage of medical data becomes a research problem due to its high sensitivity. The emergence of IoT networks formed a promising solution for data storage through the cloud server and preventing the data from various threats is a challenging problem. A secure heart disease prediction system is developed by the utility of the ESVO-based Swish Bessel CNN classifier (Emperor Spheniscidae Vampire Optimization-based Swish Bessel Convolutional Neural Network), and the important significance of the research depends on the ESVO optimization that helps in gaining a deeper insight of the classifier as well as helps in preventing the threatening of data. The security of the cloud server is enhanced by the EDH-ECC (Entropy Diffie Hellman – Elliptic Curve Cryptography) which promotes the information exchange even in unsecured channels. Similarly, the authentication and authorization of the cloud server are carried out using the EAN-13 and salt-based digital signature that initiates strong credentials and enhance data security. Finally, the heart disease is diagnosed using the ESVO-based Swish Bessel CNN classifier. Assessing the accuracy, sensitivity, specificity, and F1-measure, which provided values of 94.877 %, 95.464 %, 93.293 %, and 95.14 % shows the effectiveness of the research.

WEB BASED HEART DISEASE PREDICTION MODEL USING MACHINE LEARNING TECHNIQUE

The cases of heart diseases are increasing at a rapid rate and it’s very important to take precaution to predict any such diseases beforehand. This diagnosis is a difficult task i.e. it should be performed precisely and efficiently. The research paper mainly focuses on wen based heart disease prediction technique based on various medical attributes. Heart disease prediction system were prepared to predict whether the patient is likely to be diagnosed with a heart disease or not using the medical history of the patient. We used different algorithms of machine learning such as logistic regression and Naïve Bayes to predict and classify the patient with heart disease. A quite helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of Heart Attack in any individual. The strength of the proposed model was quiet satisfying and was able to predict evidence of having a heart disease in a particular individual by using Naïve Bayes and Logistic Regression which showed a good accuracy in comparison to the previously used classifier such as naive bayes etc. So a quiet significant amount of pressure has been lift off by using the given model in finding the probability of the classifier to correctly and accurately identify the heart disease. The Given heart disease prediction system enhances medical care and reduces the cost. This project gives us significant knowledge that can help us predict the patients with heart disease.
 Keywords: Web Based, Heart, Disease, Prediction Model, Machine Learning.

Heart Disease Prediction and Prevention System

The healthcare domain is one of the prominent research fields in the current scenario with the rapid improvement of technology and data. It is difficult to handle the huge amount of data of the patients. It is easier to handle this data through Big Data Analytics. There are a lot of procedures for the treatment of multiple diseases across the world. Machine Learning is an emerging approach that helps in prediction, diagnos of a disease. This paper depicts the prediction of disease based on symptoms using machine learning. Machine Learning algorithms such as Naive Bayes, Decision Tree and Random Forest are employed on the provided dataset and predict the disease. Its implementation is done through the Python programming language. The research demonstrates the best algorithm based on their accuracy. The performance of the given dataset determines the accuracy of an algorithm

Privacy-Enhanced Heart Disease Prediction in Cloud-Based Healthcare Systems: A Deep Learning Approach with Blockchain-Based Transmission

The increasing adoption of cloud computing in healthcare presents immense opportunities for disease prediction, while raising critical privacy concerns. This study proposes a novel privacy-preserving scheme that leverages advanced cryptographic techniques, blockchain technology and deep learning approach within a cloud platform, to ensure secure data handling and accurate disease prediction. The proposed methodology encompasses authentication, encryption, blockchain-based transmission, and a deep learning-based heart disease prediction system (HDPS). Through rigorous authentication protocols and two-level security mechanisms, patient data is securely encrypted using RSA and Blowfish encryption before storage in the cloud. Blockchain technology facilitates secure data transmission, ensuring integrity and traceability. At the receiver end, data decryption precedes input into the HDPS, comprising artificial neural networks (ANN), convolutional neural networks (CNN), and recurrent neural networks (RNN). The HDPS incorporates data preprocessing, feature extraction, feature selection, and a deep learning-based prediction model, achieving remarkable accuracy (0.9941) in heart disease prediction. Implemented in MATLAB, this approach offers a robust framework for privacy-preserving heart disease prediction in cloud-based healthcare systems.

Heart Disease Prediction System using hybrid model of Multi-layer perception and XGBoost algorithms

Multi-layer perceptron (MLP) algorithms play a critical role in improving the accuracy and effectiveness of heart disease diagnosis in the context of the machine learning research. This paper presents an approach of heart disease prediction involves RReliefF-based feature importance assessment then MLP-based classification of features into three groups based on importance scores is proposed. The study employs three feedforward neural networks to classify effectively the clustered groups. Furthermore, an integrated approach utilizes XGBoost ensemble classification, leveraging boosted ensemble learning to enhance overall classification of the outputs of FNN models. By partitioning Cleveland dataset into 70% training and 30% testing sets creates independent datasets, the incorporation of MLP outputs into the XGBoost model yields satisfied testing performance. The confusion matrix showcases accurate classifications, with 96.67% accuracy, 95.92% sensitivity, and 97.92% precision. The F1-Score, at 96.91%, validates the model's balanced performance in precision and recall. This study exemplifies the efficacy of integrating data processing, feature engineering, and ensemble learning techniques for robust cardiovascular disease prediction, providing a reliable and efficient methodology for healthcare applications.

Machine learning based heart disease prediction system

Anticipating heart illness has been one of the foremost challenging errands in medication in later a long time. Nowadays approximately one individual passes on from a heart assault each miniature. Information science plays an imperative part in handling expansive sums of information in healthcare. Since the desire of heart disease may be a troublesome errand. It is essential to total the determining prepare to maintain a strategic distance from the chance related with it and to caution patients in development. This venture employments a heart malady database with 303 persistent records and 13 parameters. This article works the hazard of heart assault utilizing distinctive learning calculations such as Calculated Relapse, Irregular Timberland, K Neighbors, and finds he leading calculation from the proper ones and returns the yield in like manner. In this way, this amplify provides a comparison by analyzing the performance of a custom learning machine.

Heart Disease Detection using Machine Learning

Abstract: Day by day the cases of heart diseases are increasing at a rapid rate and it’s very Important and concerning to predict any such diseases before hand. This diagnosis is a difficult task i.e. it should be performed precisely and efficiently. The research paper mainly focuses on which patient is more likely to have a heart disease based on various medical attributes. We prepared a heart disease prediction system to predict whether the patient is likely to be diagnosed with a heart disease or not using the medical history of the patient. We used different algorithms of machine learning such as logistic regression and KNN to predict and classify the patient with heart disease. A quite Helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of Heart Attack in any individual. The strength of the proposed model was quiet satisfying and was able to predict evidence of having a heart disease in a particular individual by using KNN and Logistic Regression which showed a good accuracy in comparison to the previously used classifier such as naïve bayes etc. So a quiet significant amount of pressure has been lifted by using the given model in finding the probability of the classifier to correctly and accurately identify the heart disease. The Given heart disease prediction system enhances medical care and reduces the cost. This project gives us significant knowledge that can help us predict the Patient's Heart disease.

An energy-aware heart disease prediction system using ESMO and optimal deep learning model for healthcare monitoring in IoT

ABSTRACT: The Internet of Things (IoT), which provides seamless connectivity between people and things, improves our quality of life. In the medical field, predictive analytics can help transform a reactive healthcare (HC) strategy into a proactive one. The HC industry embraces cutting-edge artificial intelligence and machine learning (ML) technologies. ML’s area of deep learning has the revolutionary potential to reliably analyze massive volumes of data quickly, produce insightful revelations and solve challenging issues. This article proposes an energy-aware heart disease prediction (HDP) system based on enhanced spider monkey optimization (ESMO) and a weight-optimized neural network for an IoT-based HC environment. The proposed work consists of two essential phases: energy-efficient data transmission and HDP. In energy-efficient transmission, the cluster leaders are optimally selected using ESMO and the cluster formation is done based on Euclidean distance. In HDP, the patient data are collected from the dataset, and essential features are extracted. After that, the dimensionality reduction is carried out using the modified linear discriminant analysis approach to reduce over-fitting issues. Finally, the HDP uses the enhanced Archimedes weight-optimized deep neural network (EAWO-DNN). The simulation findings demonstrate that the proposed optimal clustering mechanism enhances the network’s lifespan by consuming minimal energy compared to the existing techniques. Also, the proposed EAWO-DNN classifier achieves higher prediction accuracy, precision, recall and f-measure than the conventional methods for predicting heart disease in IoT. Communicated by Ramaswamy H. Sarma

An Efficient Filter and Wrapper based Selection Methods along With Random Forest and Support Vector Machines Classification Technique in Health Care System

Health care Management System (HMS) is a key to successful management of any health care industry. Health care management systems have so many research dimensions such as identifying disease and diagnostic, drug discovery manufacturing, Bioinformatics’ problem, personalized treatments, Patient image analysis and so on. Heart Disease Prediction (HDP) is a process of identifying heart disease in advance and recognizes patient health condition by applying techniques on patient heart related symptoms. Now a day’s the problem of identifying heart diseases is solved by machine learning techniques. In this paper we construct a heart disease prediction method using combined feature selection and classification machine learning techniques. According to the existing study the one of the main difficult in heart disease prediction system is that the available data in open sources are not properly recorded the necessary characteristics and there is some lagging in finding the useful features from the available features. The process of removing inappropriate features from an available feature set while preserving sufficient classification accuracy is known as feature selection. A methodology is proposed in this paper that consists of two phases: Phase one employs two broad categories of feature selection techniques to identify the efficient feature sets and it is given to the input of our second phase such as classification. In this work we will concentrate on filter-based method for feature selection such as Chi-square, Fast Correlation Based Filter (FCBF), Gini Index (GI), RelifeF, and wrapper-based method for feature selection such as Backward Feature Elimination (BFE), Exhaustive Feature Selection (EFS), Forward Feature Selection (FFS), and Recursive Feature Elimination (RFE). The UCI heart disease data set is used to evaluate the output in this study. Finally, the proposed system's performance is validated by various experiments setups.

Exploiting Neural Network for Heart Disease Probability Prediction

In general, doctors determine the presence of heart disease through clinical evaluation and pathological data, and the diagnosis process is complex and inefficient. Based on the above situation, professionals are committed to researching efficient and accurate methods for predicting heart disease. After studying many literatures, this paper found that the existing heart disease prediction system has high requirements for clinical data. Based on the reality of the shortage of medical resources under the COVID-19 epidemic, this paper develops a simple heart disease prediction system, which predicts heart disease through simple and easy-to-measure data of patients, and then prevents heart disease. The method consists of two steps. First, collect the characteristics related to heart disease, and then select the most important 10 characteristics through correlation analysis and literature research, namely gender, age range, body mass index (BMI), smoking status, physical health index, walking difficulty, stroke status, skin cancer, diabetes, kidney disease. Second, an algorithm for heart disease based on artificial neural networks classification based on these features is developed. The prediction accuracy is close to 92%. In the future, the proposed model could be leveraged for heart disease recognition.

Review on Cleveland Heart Disease Dataset using Machine Learning

According to the World Health Organization (WHO), heart disease has been a foremost source of death worldwide for the past 15 years. Therefore Medical diagnosis is typically performed mostly by doctors due to their training and experience. In the field of medicine, computer-aided decision support systems are enormously significant. Therefore, it is necessary to develop prediction systems that give information of different categories to readers. According to the study, hybrid intelligent algorithms increase the heart disease prediction system’s accuracy. Hence, recognizing cardiovascular problems including heart attacks, coronary artery diseases, etc. by routine clinical data analysis is an important task; early identification of heart disease may save many lives. In this article, have reviewed various papers related to the Cleveland heart disease dataset that used one or more machine-learning algorithms to forecast congestive heart failure. In one of the above mention papers, the result of utilizing Random Forest is almost 100%. To ensure that predictions made using machine learning algorithms produce accurate outcomes. Applying machine learning algorithms to heart disease treatment data can produce results that are just as accurate as those found in heart disease diagnosis.

IoT-based patient monitoring system for predicting heart disease using deep learning

Motivation: Chronic diseases include diabetes, cancer, heart disease (HD), and chronic respiratory diseases are the main causes of mortality globally. It is quite challenging to identify heart diseases when the symptoms or characteristics vary. In the contemporary digital environment, the healthcare sector produces a considerable volume of patient data. For doctors, manually processing these created data becomes exceedingly challenging. The Internet of Things is handling the generated data quite well. It provides continuous communication between individuals and devices, and its fusion with the Cloud enhances the quality of life. Materials and Methods: Deep learning, a branch of machine learning, has the transformational ability to rapidly and reliably analyse massive amounts of data, produce insightful conclusions, and effectively resolve complex problems. Massive volumes of data were collected by the IoT, and because of deep-learning algorithms, it is now possible to identify and diagnose diseases. The suggested approach collects information from IoT devices, and electronic medical evidence connected to patient histories that are stored in the cloud are sent to predictive analytics. Results and Conclusion: The Long Short Term Memory (LSTM) and Recurrent Neural Network based smart healthcare system for monitoring and precisely forecasting heart diseases obtains an accuracy of 99.99%, which is substantially superior to the current smart heart disease prediction systems such as traditional methods.

Machine Learning in Cardiology: a Survey of Early Detection Models for Heart Diseases

Heart disease detection and early prediction is one of the most difficult tasks in the medical field. Almost two people die every minute due to cardio vesicular diseases. According to World Health Organization (WHO), 17.9 million people depart their life every year out of which 4.77 million people are from India alone. About 13% of the world's total population is involved in cardiac disease. Early detection of the disease is crucial for effective treatment that can save millions of lives in the world. Traditional methods of heart disease detection typically involve a combination of medical history, physical examination, and diagnostic tests which are less accurate. With the advancements in machine learning and deep learning techniques, the development of accurate prediction models for heart disease has become possible. Nowadays a large volume of data is being generated in the healthcare sector, which can be leveraged to empower the development of accurate prediction models for heart diseases. Various techniques such as logistic regression, decision trees, random forest, support vector machine, artificial neural networks, and convolutional neural networks have been applied to predict heart diseases. Over the years, advancements in medical technology have led to the development of new diagnostic tools and techniques for detecting heart disease. In this study, a comparative analysis of these techniques is carried out to understand the architectures, parametric characteristics, and datasets involved in heart disease prediction. Our analysis indicates that most heart disease prediction system that have been designed using deep learning algorithms show promising performance.

CFLCA: High Performance based Heart disease Prediction System using Fuzzy Learning with Neural Networks

Human Diseases are increasing rapidly in today’s generation mainly due to the life style of people like poor diet, lack of exercises, drugs and alcohol consumption etc. But the most spreading disease that is commonly around 80% of people death direct and indirectly heart disease basis. In future (approximately after 10 years) maximum number of people may expire cause of heart diseases. Due to these reasons, many of researchers providing enormous remedy, data analysis in various proposed technologies for diagnosing heart diseases with plenty of medical data which is related to heart disease. In field of Medicine regularly receives very wide range of medical data in the form of text, image, audio, video, signal pockets, etc. This database contains raw dataset which consist of inconsistent and redundant data. The health care system is no doubt very rich in aspect of storing data but at the same time very poor in fetching knowledge. Data mining (DM) methods can help in extracting a valuable knowledge by applying DM terminologies like clustering, regression, segmentation, classification etc. After the collection of data when the dataset becomes larger and more complex than data mining algorithms and clustering algorithms (D-Tree, Neural Networks, K-means, etc.) are used. To get accuracy and precision values improved with proposed method of Cognitive Fuzzy Learning based Clustering Algorithm (CFLCA) method. CFLCA methodology creates advanced meta indexing for n-dimensional unstructured data. The heart disease dataset used after data enrichment and feature engineering with UCI machine learning algorithm, attain high level accurate and prediction rate. Through this proposed CFLCA algorithm is having high accuracy, precision and recall values of data analysis for heart diseases detection.

Heart Disease Prediction and Treatment Suggestion Using Machine Learning

Disease anticipation systems are the better alternatives, to avoid the human errors in disease diagnosis and also assist in disease interference. Nowadays, the number of heart disease patients is increasing so we need an optimal heart disease prediction and treatment suggestion system. Heart disease dataset preparation, prediction system’s process flow design, process execution and results evaluation are the most common life cycle modules of any heart disease prediction system. Input dataset attributes modeling, attribute risk factor calculation; threshold determination and achieving the high accuracy in disease prediction are the major limitations of the existing heart disease prediction and treatment proposal systems. Keywords: Machine learning, Decision tree, Logistic regression.