Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare (original) (raw)
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IRJET- Human Heart Condition Prediction using Machine Learning
IRJET, 2021
Heart disease is one of the complex diseases and globally many people suffered from this disease. On time and efficient identification of heart disease plays a key role in healthcare, particularly in the field of cardiology. In this article, we proposed an efficient and accurate system to diagnosis heart disease and the system is based on machine learning techniques. The system is developed based on classification algorithms includes Support vector machine, Logistic regression, Artificial neural network, K-nearest neighbor, Naïve bays, and Decision tree while standard features selection algorithms have been used such as Relief, Minimal redundancy maximal relevance, Least absolute shrinkage selection operator and Local learning for removing irrelevant and redundant features. We also proposed novel fast conditional mutual information feature selection algorithm to solve feature selection problem. The features selection algorithms are used for features selection to increase the classification accuracy and reduce the execution time of classification system. Furthermore, the leave one subject out cross-validation method has been used for learning the best practices of model assessment and for hyperparameter tuning. The performance measuring metrics are used for assessment of the performances of the classifiers. The performances of the classifiers have been checked on the selected features as selected by features selection algorithms. The experimental results show that the proposed feature selection algorithm (FCMIM) is feasible with classifier support vector machine for designing a high-level intelligent system to identify heart disease. The suggested diagnosis system (FCMIM-SVM) achieved good accuracy as compared to previously proposed methods. Additionally, the proposed system can easily be implemented in healthcare for the identification of heart disease.
Applied Computational Intelligence and Soft Computing
Heart disease is recognized as one of the leading factors of death rate worldwide. Biomedical instruments and various systems in hospitals have massive quantities of clinical data. Therefore, understanding the data related to heart disease is very important to improve prediction accuracy. This article has conducted an experimental evaluation of the performance of models created using classification algorithms and relevant features selected using various feature selection approaches. For results of the exploratory analysis, ten feature selection techniques, i.e., ANOVA, Chi-square, mutual information, ReliefF, forward feature selection, backward feature selection, exhaustive feature selection, recursive feature elimination, Lasso regression, and Ridge regression, and six classification approaches, i.e., decision tree, random forest, support vector machine, K-nearest neighbor, logistic regression, and Gaussian naive Bayes, have been applied to Cleveland heart disease dataset. The feat...
Adaptive Features Selection Technique for Efficient Heart Disease Prediction
Journal of Al-Qadisiyah for Computer Science and Mathematics
Heart disease is a common disease that causes death and is difficult to detect manually. A more efficient classification model that relies on machine learning methods to achieve higher classification accuracy, attracts the attention of researchers to design an effective prediction model. Moreover, it plays an important role in the practical application of medical cardiology with the aim of early detection of heart diseases. In this paper, an efficient and accurate heart disease detection system is proposed based on the proposed adaptive feature selection technique using four machine learning methods: Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF). Two feature selection methods were used to design the proposed technique, mutual information (MI) and recursive feature elimination (RFE) to determine the optimal number of selected features that increase the performance of the classification models and reduce the time complexity of model...
IEEE Access, 2022
Predicting heart disease is regarded as one of the most difficult challenges in the health-care profession. To predict cardiac disease, researchers employed a variety of algorithms including LDA, RF, GBC, DT, SVM, and KNN, as well as the feature selection algorithm sequential feature selection. For verification, the system employs the K-fold cross-validation approach. These six strategies were used to conduct the comparative study. The Dataset for Cleveland, Hungray, Switzerland, and Long Beach V, as well as the Dataset Heart Statlog Cleveland Hungary, were used to assess the models performance. For both Hungary, Switzerland & Long Beach V and Heart Statlog Cleveland Hungary Dataset, Random Forest Classifier sfs and Decision Tree Classifier sfs produced the highest and almost identical accuracy values (100%, 99.40% and 100%, 99.76% respectively). The findings were compared to previous research that focused on cardiac prediction. In the future, we hope to extend the model even further so that it may be used with various feature selection techniques; another possibility is to use a random forest classifier. The major goal of this study is to improve on previous work by developing a new and unique technique for creating the model, as well as to make the model relevant and easy to use in real-world situations.
Designs
Automatic heart disease prediction is a major global health concern. Effective cardiac treatment requires an accurate heart disease prognosis. Therefore, this paper proposes a new heart disease classification model based on the support vector machine (SVM) algorithm for improved heart disease detection. To increase prediction accuracy, the χ2 statistical optimum feature selection technique was used. The suggested model’s performance was then validated by comparing it to traditional models using several performance measures. The proposed model increased accuracy from 85.29% to 89.7%. Additionally, the componential load was reduced by half. This result indicates that our system outperformed other state-of-the-art methods in predicting heart disease.
Efficient Classification of Heart Disease Using Machine Learning Algorithm
2021
Heart disease is the one of the major disease and many human beings suffered without any symptoms. In healthcare, especially in finding of heart disease in particular time plays a crucial role in cardiology area. In this paper, we proposed an effective and perfect system to predict heart disease system based on machine learning systems. This system is organized by using various classification algorithms such as LR,KNN,SVM,DT,NB and RF. The proposed algorithmic technique also solve the problem of feature selection and increase the accuracy of classification. In addition with that, the proposed algorithmic technique could use non-invasive clinical data for the heart Disease diagnosis and assessing its severity. The implementation of novel hybrid method helps to improve the accuracy of the EDA diagnosis. The proposed novel hybrid method result shows high accuracy of data is compared to previously proposed techniques. In addition to that, the proposed system is easily be adapted with the existing technology.in this proposed system technology more the 300 instance are collected and the results are compared with existing technology. The results prove that it has more accuracy and it can be easily implemented to identify CAD disease in healthcare field.
IJERT-Effective Prediction Model for Heart Disease Using Machine Learning Algorithm
International Journal of Engineering Research and Technology (IJERT), 2019
https://www.ijert.org/effective-prediction-model-for-heart-disease-using-machine-learning-algorithm https://www.ijert.org/research/effective-prediction-model-for-heart-disease-using-machine-learning-algorithm-IJERTCONV7IS01045.pdf Data mining is the process of discovering interesting knowledge, such as patterns, associations, changes, anomalies and significant structures from large amounts of data stored in databases and data warehouses. The most basic definition of data mining is the analysis of large data sets to discover patterns and use those patterns to forecast or predict the likelihood of future events. Data Mining is one of the most critical aspects of automated disease diagnosis and disease prediction. It involves developing data mining algorithms and techniques to analyze medical data. In present, heart disease has excessively increased and heart diseases are becoming one of the most fatal diseases in several countries. In this paper, heart patient datasets are investigate for building classification models in order to predict heart diagnosis. This paper implements feature model construction and comparative analysis for improving prediction accuracy of heart disease in three phases. In first phase, normalization algorithms are applied on the heart patient datasets collected from UCI repository. In second phase, by the use of HCR-PSO (Highly Co-Related Attribute Practical Swarm Optimization) feature selection, using a subset (data) of heart patient from whole normalized heart patient datasets is obtained which comprises only significant attributes and then applying selected classification algorithms on obtained, significant subset of attributes.Third phase, classification algorithm KNN, Random forest, J48, SVM, Bayesian network and MLP algorithm is considered as the better performance algorithm, because it gives higher accuracy in respective to other classification analytical Model before applying HCR-PSO feature selection. But, J48 algorithm is considered as the better performance algorithm after applying HCR-PSO feature selection. In third phase, the results of classification algorithms with and MAE and RMSE validation metric are compared with each other. The results obtained from our experiments indicate that J48 algorithm outperformed all other techniques with the help of feature selection with an accuracy of 94.40%. .
Cardiovascular Disease Prognosis Using Effective Classification and Feature Selection Technique
2016
Cardiovascular disease is a worldwide health problem and according to American Heart Association (AHA), it also causes an approximate death of 17.3 million each year. Therefore early detection and treatment of asymptomatic cardiovascular disease which can significantly reduce the chances of death. An important fact regarding such life-threatening disease prognosis is to identify the patient's physical state (healthy or sick) based on the analysis of health checkup data. This paper aims at optimized cardiovascular disease prognosis using different data mining techniques. We also provide a technique to improve the accuracy of proposed classifier models using feature selection technique. Patient's data were collected from Department of Computing of Goldsmiths University of London. This dataset contains total 14 attributes in which we applied SMO (SVM-Support Vector Machine), C4.5 (J48-Decision Tree) and Naïve Bayes classification algorithms and calculated their prediction accuracy. An efficient feature selection algorithm helped us to improve the accuracy of each model by reducing some lower ranked attributes. Which helped us to gain an accuracy of 87.8%, 86.80% & 79.9% in case of SMO, Naïve Bayes and C4.5 Decision Tree algorithms respectively.
Improvement of heart attack prediction by the feature selection methods
TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, 2018
Prediction of a heart attack is very important since it is one of the leading causes of sudden death, especially in low-income countries. Although cardiologists use traditional clinical methods such as electrocardiography and blood tests for heart attack prediction, computer aided diagnosis systems that use machine learning methods are also in use for this task. In this study, we used machine learning and feature selection algorithms together. Our aim is to determine the best machine learning method and the best feature selection algorithm to predict heart attacks. For this purpose, many machine learning methods with optimum parameters and several feature selection methods were used and evaluated on the Statlog (Heart) dataset. According to the experimental results, the best machine learning algorithm is the support vector machine algorithm with the linear kernel, while the best feature selection algorithm is the reliefF method. This pair gave the highest accuracy value of 84.81%.
Computational Intelligence and Neuroscience
Coronary heart disease has an intense impact on human life. Medical history-based diagnosis of heart disease has been practiced but deemed unreliable. Machine learning algorithms are more reliable and efficient in classifying, e.g., with or without cardiac disease. Heart disease detection must be precise and accurate to prevent human loss. However, previous research studies have several shortcomings, for example,take enough time to compute while other techniques are quick but not accurate. This research study is conducted to address the existing problem and to construct an accurate machine learning model for predicting heart disease. Our model is evaluated based on five feature selection algorithms and performance assessment matrix such as accuracy, precision, recall, F1-score, MCC, and time complexity parameters. The proposed work has been tested on all of the dataset'sfeatures as well as a subset of them. The reduction of features has an impact on theperformance of classifiers...