Heart Disease Prediction System Research Articles

A Gradient Boosted Decision Tree with Binary Spotted Hyena Optimizer for cardiovascular disease detection and classification

Cardiovascular disease (CVD) is a common disorder frequently resulting in death. An increase in the death rate among adults is attributed to several factors, including smoking, high blood pressure, obesity, and cholesterol. Early diagnosis of CVDs can lower mortality rates. Algorithms that use machine learning and data mining offer the potential for finding risk variables and predicting CVD. Developing countries often need more CVD experts, and a high percentage of misdiagnosis. These concerns could be alleviated using an accurate and effective early-stage heart disease prediction system. This study explores the effectiveness of machine learning classifiers for diagnosing and detecting CVD. Several supervised machine-learning algorithms are investigated, and their performance and accuracy are compared. The Gradient Boosted Decision Tree (GBDT) with Binary Spotted Hyena Optimizer (BSHO) suggested in this work was used to rank and classify all attributes. Discrete optimization problems can be resolved using the binary form of SHO. The recommended method compresses the continuous location using a hyperbolic tangent function. The updated spotted hyena positions on the relevance score are utilized to find those with high heart disease predictions. The efficiency of the suggested model is then confirmed using the UCI dataset. The proposed GBDT-BSHO approach, with an accuracy of 97.89%, was significantly more effective than the comparative methods.

Prediksi Penyakit Jantung Dengan Algoritma Regresi Linier

In this study, we evaluated the ability of a linear regression algorithm to predict heart disease risk in individuals. We use data from trusted sources and perform the necessary preprocessing to clean and provide the data for the model. The results of the analysis show that the linear regression algorithm can be used well to predict the risk of heart disease in individuals with a fairly high degree of accuracy. We also evaluated several factors that influence heart disease risk and demonstrated that they could be identified and integrated into our model to improve its performance. In addition, we also evaluated the validation methods used to evaluate our models and demonstrated that they can be used to objectively determine model performance. The results from this study provide a solid foundation for developing a better heart disease prediction system in the future. And the results of this study are quite accurate enough to give good results with a Root Mean Squared Error: 0.379 +/- 0.000 and Squared Error: 0.144 +/- 0.229

Machine Learning Based Heart Disease Prediction System

Abstract: Heart attack disease is one of the leading causes of the death worldwide. In today’s common modern life, deaths due to the heart disease had become one of major issues, that roughly one person lost his or her life per minute due to heart illness. Predicting the occurrence of disease at early stages is a major challenge nowadays. Machine learning when implemented in health care is capable of early and accurate detection of disease. In this work, the arising situations of Heart Disease illness are calculated. Datasets used have attributes of medical parameters. The datasets are been processed in python using ML Algorithm i.e., Random Forest Algorithm. This technique uses the past old patient records for getting prediction of new one at early stages preventing the loss of lives. In this work, reliable heart disease prediction system is implemented using strong Machine Learning algorithm which is the Random Forest algorithm, which read patient record data set in the form of CSV file. After accessing dataset the operation is performed and effective heart attack level is produced. Advantages of proposed system are High performance and accuracy rate and it is very flexible and high rates of success are achieved

An Innovative Penalty based Heart Disease Prediction system using Novel Random Forest over Logistic Regression Classifier Algorithm

Aim: The main goal of the research is see how accurately predicting heart disease by Logistic Regression (LR) and Novel Random Forest(RF) Classifications. Materials and Methods: Novel Random forest appealed on a heart dataset which consists of 200 records A framework for predicting heart disease in the medical field has been proposed and developed to compare the RF with a LR classifier. The sample size was calculated to be 55 for each group with 80% G performance. The sample size was calculated using a Clincalc analysis with Alpha and Beta values of 0.05 and 0.5, pretest performance of 80%, and enrollment rate of 1. The Accuracy of the classifier was Evaluated and Recorded. Results: The LR produces 89.0% in predicting the heart disease on the data set used whereas the Novel Random forest classifier predicts the same at the rate of 95.46% of the time with a statistically significant difference between the two groups (P=0.03; P<0.05) with confidence interval 95%. Conclusion: RF is better compared with LR in terms of both precision and accuracy.

Performance Analysis of Heart Disease Prediction System using Novel Random Forest Over Naive Bayes Algorithm with an Improved Accuracy Rate

Aim: The cardiac is a vital organ in the human body. Life is entirely dependent on the functional workings of the heart. Cardiovascular disease refers to diseases of the cardiac and the blood vessels that supply it. The primary goal of this article is to perform on the exactness of the arrangement of coronary disease expectation utilizing the assistance of AI calculations. We will be comparing the novel Random forest with Naive Bayes to find out which of these can give us the best accuracy. Material and Methods: The study used 1402 samples with Novel Random Forest and Naive Bayes is executed with varying training and testing splits for foreseeing the exactness for coronary illness forecast with the 80% of G-power value and cardiovascular disease information were collected from various website domains using the most recent research and a threshold of 0.05%, a 95% uncertainty range, a mean, and a confidence interval. The accuracy rate of the classifiers is used to evaluate their performance using the heart disease dataset. A statistically insignificant difference exists among the two groups p=0.199; p>0.05. Results and Discussion: The accuracy of predicting heart disease in Novel Random Forest 90.16% and Naive Bayes 85.25% is obtained. Conclusion: Prediction of Heart disease using With enhanced accuracy, the novel Random Forest(RF) the technique appears to be far better to the Naive Bayes(NB).

Healthcare Monitoring System for Remote Areas

Abstract: Doctors now place a high importance on ongoing patient health monitoring since it gives them the chance to save a patient's life. So, the primary objective is to develop a patient monitoring system that can monitor a patient's various physiological data when they are in a remote location and provide the doctor with this information in real time. The information is made public online so that any doctor in the globe can access it. The necessity for a patient to visit the doctor is lessened via remote patient monitoring. The Raspberry Pi employed here is not only a sensor node but also a CPU, and IOT plays a significant part in this complete system by delivering several apps and services. This data can be sensed, gathered, and published online by an intelligent gadget. The paper suggests a general health monitoring system utilising a neural network-based HDPS (Heart Disease Prediction System). The HDPS system forecasts a patient's risk of developing heart disease. The technique uses medical parameters like sex, blood pressure, age, height, and weight for prediction. as an improvement over the work done in this area up until now.

Retracted: Smart Heart Disease Prediction System with IoT and Fog Computing Sectors Enabled by Cascaded Deep Learning Model.

[This retracts the article DOI: 10.1155/2022/1070697.].

Intelligent Heart Disease Prediction System with Applications in Jordanian Hospitals

Intelligent Heart Disease Prediction System with Applications in Jordanian Hospitals

Feedback Mechanism in Heart Disease Classification

Heart diseases are becoming more common every day, making it important and concerning to predict and diagnose these conditions accurately and efficiently. This research paper focuses on identifying patients who are more likely to have a heart disease based on various medical attributes and also examines which attributes are contributing to the heart disease. To address this issue, we developed a heart disease prediction system that uses machine learning algorithms, such as logistic regression, to predict and classify patients with heart disease. This system helps to relieve a significant amount of pressure on medical professionals by accurately identifying the likelihood of a patient having a heart disease. In addition, the heart disease prediction system improves medical care and reduces costs. Overall, this project provides valuable knowledge that can aid in the prediction of heart disease in patients and is implemented in .pynb format.

Heart Disease Prediction; a Machine Learning Approach

Machine learning has been one of the most widely used tools in medical science. It has shown promising application in disease detection as well as in disease prediction. In this paper we focus on the artificial intelligence-based heart disease prediction system using machine learning algorithms. We discuss various algorithms that have been employed by researchers in healthcare sector and chalk out the comparative analysis among these various algorithms. Traditional clinical diagnosis methods have their own limitations, with the help of our study we would be focusing on accuracy and ability to detect the heart disease at early stage. We have discussed some most recent works in heart disease prediction and done a comparative analysis on the accuracy and fusibility of these methods.

A Robust Framework Combining Image Processing and Deep Learning Hybrid Model to Classify Cardiovascular Diseases Using a Limited Number of Paper-Based Complex ECG Images.

Heart disease can be life-threatening if not detected and treated at an early stage. The electrocardiogram (ECG) plays a vital role in classifying cardiovascular diseases, and often physicians and medical researchers examine paper-based ECG images for cardiac diagnosis. An automated heart disease prediction system might help to classify heart diseases accurately at an early stage. This study aims to classify cardiac diseases into five classes with paper-based ECG images using a deep learning approach with the highest possible accuracy and the lowest possible time complexity. This research consists of two approaches. In the first approach, five deep learning models, InceptionV3, ResNet50, MobileNetV2, VGG19, and DenseNet201, are employed. In the second approach, an integrated deep learning model (InRes-106) is introduced, combining InceptionV3 and ResNet50. This model is developed as a deep convolutional neural network capable of extracting hidden and high-level features from images. An ablation study is conducted on the proposed model altering several components and hyperparameters, improving the performance even further. Before training the model, several image pre-processing techniques are employed to remove artifacts and enhance the image quality. Our proposed hybrid InRes-106 model performed best with a testing accuracy of 98.34%. The InceptionV3 model acquired a testing accuracy of 90.56%, the ResNet50 89.63%, the DenseNet201 88.94%, the VGG19 87.87%, and the MobileNetV2 achieved 80.56% testing accuracy. The model is trained with a k-fold cross-validation technique with different k values to evaluate the robustness further. Although the dataset contains a limited number of complex ECG images, our proposed approach, based on various image pre-processing techniques, model fine-tuning, and ablation studies, can effectively diagnose cardiac diseases.

IoT-Cloud-Based Smart Healthcare Monitoring System for Heart Disease Prediction via Deep Learning

The Internet of Things confers seamless connectivity between people and objects, and its confluence with the Cloud improves our lives. Predictive analytics in the medical domain can help turn a reactive healthcare strategy into a proactive one, with advanced artificial intelligence and machine learning approaches permeating the healthcare industry. As the subfield of ML, deep learning possesses the transformative potential for accurately analysing vast data at exceptional speeds, eliciting intelligent insights, and efficiently solving intricate issues. The accurate and timely prediction of diseases is crucial in ensuring preventive care alongside early intervention for people at risk. With the widespread adoption of electronic clinical records, creating prediction models with enhanced accuracy is key to harnessing recurrent neural network variants of deep learning possessing the ability to manage sequential time-series data. The proposed system acquires data from IoT devices, and the electronic clinical data stored on the cloud pertaining to patient history are subjected to predictive analytics. The smart healthcare system for monitoring and accurately predicting heart disease risk built around Bi-LSTM (bidirectional long short-term memory) showcases an accuracy of 98.86%, a precision of 98.9%, a sensitivity of 98.8%, a specificity of 98.89%, and an F-measure of 98.86%, which are much better than the existing smart heart disease prediction systems.

IoT Based Elderly Healthcare Monitoring System

Abstract: Now a days continuous patient health monitoring is becoming a key concern for the doctors since it can help them save patient’s life. So, the main objective is to design a patient monitoring system which can monitor various physiological data of patient who is remotely placed and make available this real time data to the doctor. The data is made available over the internet so that doctor can access this data from anywhere in the world. Remote patient monitoring reduces the need of doctor`s visit to the patient. In this entire system IOT plays a major role in providing many applications and services and Raspberry Pi used here is not just a sensor node but also a processor. It is a smart device which can sense, gather and publish this data over the internet. Paper proposes a generic health monitoring system with Heart Disease Prediction system (HDPS) using Neural network. The HDPS system predicts the likelihood of patient getting a heart disease. For prediction, the system uses sex, blood pressure, age, height, weight like medical parameters. as a step forward to the progress made in this department till now.

Heart Disease Prediction using Machine Learning

Abstract: The term "heart disease" refers to any heart disease or condition that can cause heart problems. Cardiovascular disease (CVD) is the leading cause of death worldwide, taking many lives each year. CVD is a group of cardiovascular diseases and includes heart disease, cerebrovascular disease, rheumatic heart disease and other conditions. According to the World Health Organization (WHO), more than 17.9 million people worldwide die each year from coronary heart disease. If we take the example of India, every year the number of deaths due to heart disease has increased. Studies show that, from 2014 to 2019 the number of deaths from heart disease increased by 53%. Many threatening factors such as personal and work habits and genetic predisposition are major causes of heart disease. A variety of harmful habits such as smoking, alcohol and caffeine overdose, stress, and inactivity as well as other physical factors such as obesity, high blood pressure, high blood cholesterol, and pre-existing heart conditions are the main causes of heart disease. Over time, these harmful substances cause changes in the heart and blood vessels that can lead to heart attacks and strokes. Therefore, prevention of heart disease is very important to prevent these dangerous events and other potential complications of heart disease. Machine learning is a flexible part of AI that helps predict heart disease. In this research work, we will use the UCI database with 14 attributes to predict heart disease. The main goal of this study is to use ML algorithms to improve the heart disease prediction system and to more accurately predict these diseases in patients, thereby reducing the number of deaths by alerting patients. Keywords: Heart Diseases, Classification Algorithms, Machine Learning, UCI dataset.

Prediction for Effective Heart Disease System

Abstract: The health care industries gather large quantities of records that include a few hidden information, which is beneficial for making powerful choices. For imparting suitable effects and making powerful choices on records, a few superior records mining strategies are used. In this study, a Heart Disease Prediction System (HDPS) evolved the use of Naive Bayes and Decision Tree algorithms for predicting the danger stage of a coronary heart ailment. The device makes use of 15 scientific parameters along with age, sex, blood pressure, cholesterol, and weight problems for prediction. The HDPS predicts the chance of sufferers getting coronary heart ailment. It permits substantial knowledge. Relationships among scientific elements associated with coronary heart ailment and patterns, to be established. We have hired the multilayer perceptron neural community with backpropagation because of the education algorithm. The acquired effects have illustrated that the designed diagnostic device can efficiently predict the dangerous stage of coronary heart disease.

Heart Disease Prediction System Based on Multiple Feature Selection Algorithm with Ensemble Classifier

In today's world, deaths from heart disease have become a significant problem. Nearly one person dies from heart disease every minute. In most situations, the diagnosis of cardiac disease is based on a complicated mix of clinical and pathological data. This complexity leads to expensive medical costs, which have an impact on the quality of medical care. The intention of the work is to predict the heart disease based on medical records. The proposed method employs data mining classification methods like KNN, SVM, and Decision Tree to find out knowledge in a heart disease dataset, which is obtained from UCI Machine learning repository. The input dataset consists of 282 observations with 75 attributes. The feature selection of input dataset is done using Multiple Feature Selection Algorithm (MFSA), which uses Auto-Correlation and Information gain to find the best set of features and improves the performance of the classifiers. The results are obtained from classification methods with and without using Feature Selection in the form of accuracy. Based on the experimental analysis, classification with Feature Selection method outperformed classification without Feature Selection method.

Artificial Intelligence Ensemble Methods for Cardiovascular Disease Prediction

Recently, the main cause of death occurring due to cardiovascular disease has happened even in the past. Early diagnosis of the disease can be assessed to reduce the high risk and ensure healthiness. Data mining techniques have been significantly used as it helps in zero or less intervention of humans and it is seen as the best technique as it gives precise result with the best accuracy. The study is conducted on ensemble methods and built a model using boosting and bagging classifiers. The objective of this work is to design and implement a heart disease prediction system using Artificial Intelligence ensemble methods namely, Random Forest, Adaptive Boosting, Gradient Boosting, and XGBoost. The effective performance of the applied ensemble techniques is analyzed, and a mobile application is developed for the same. The proposed mobile application is built as a user interface that accepts data based on clinical attributes concerning heart disease. This mainly helps in the medical field, such as laboratories that incorporate the developed model. The outcome of the proposed model predicts the probability of a person suffering from heart disease. The accuracy of the models is evaluated.

Effective Prediction of Heart Disease Through Machine Learning

In present times, in the field of healthcare, identification of heart disease is difficult. Just because of this heart disease, around one person passes away per minute in the modern era. There is a requirement of automation of prediction system to avoid hazard related with it as well as aware the patient’s health in advance. There are various ML techniques that have been exposed to be helpful in supporting predictions and making decisions from the huge data which is generated through the medical industries. In this article, we offer a new system that intends to focus on important features through concerning machine learning methods that result in improved accurateness within the heart disease prediction. The proposed model is established by special features combinations and numerous recognized classification methods. We generate an improved performance level through an above 80% accuracy level throughout the proposed model intended for heart disease prediction system by the hybrid model. Therefore, this article represents a relative learning by evaluating the performance of various machine learning techniques.

An Integrated Machine Learning Model for Heart Disease Classification and Categorization

Progressions in the coordination among the machine learning algorithms, helped to achieve high accuracy and reliability in decision-making systems. Due to the impact and importance of heart diseases in real life, designing the efficient heart disease prediction model become a pivotal aspect today. Former research scholars applied the popular supervised machine learning models like Decision Trees, Naïve Bayes, ANN's, and FNN's to implement the heart disease prediction systems. As the heart disease prediction process is a multi-layered operation, each layer is expecting the optimal machine learning algorithm and the coordination among the algorithms of different layers to minimize the errors in prediction results. In this study, we proposed a new fuzzy neural-genetic algorithm to design an efficient and accurate heart disease prediction system. In our prediction system, we integrated the genetic algorithm, neural networks, and fuzzy logic technologies for training, classification, and categorization processes respectively. Experimental evaluations of Cleveland's heart disease dataset proved that the proposed fuzzy neural-genetic algorithm-based prediction system achieved high accuracy and low error rate when compared with the other machine learning models.

Heart Disease Prediction Systems and a comparison of methods to improve Public healthcare

Around 17.9 million people die of cardiovascular diseases every year. 80% of the deaths occur in low and middle-income countries. Most of the population doesn’t have sufficient resources to conduct expensive checkups to detect such diseases. As a result, the disease manifest and then ends up taking countless innocent lives. Consequently, there is a need to build up some strategy/way with the goal that it is conceivable to foresee the ailments inexpensively. In this paper, a comparison between the different types of heart disease prediction systems, based on their precision, accuracy, and reliability has been discussed. Methods like MLP, SVM, ANN, Random Forests have been used to early predict heart diseases in individuals.