A Machine Learning Framework for Feature Selection in Heart Disease Classification Using Improved Particle Swarm Optimization with Support Vector Machine Classifier
TL;DR: A novel function for identifying optimal weights on the basis of population diversity function and tuning function and a novel fitness function for PSO with the help of Support Vector Machine (SVM) are presented.
Abstract: Machine learning is used as an effective support system in health diagnosis which contains large volume of data. More commonly, analyzing such a large volume of data consumes more resources and execution time. In addition, all the features present in the dataset do not support in achieving the solution of the given problem. Hence, there is a need to use an effective feature selection algorithm for finding the more important features that contribute more in diagnosing the diseases. The Particle Swarm Optimization (PSO) is one of the metaheuristic algorithms to find the best solution with less time. Nowadays, PSO algorithm is not only used to select the more significant features but also removes the irrelevant and redundant features present in the dataset. However, the traditional PSO algorithm has an issue in selecting the optimal weight to update the velocity and position of the particles. To overcome this issue, this paper presents a novel function for identifying optimal weights on the basis of population diversity function and tuning function. We have also proposed a novel fitness function for PSO with the help of Support Vector Machine (SVM). The objective of the fitness function is to minimize the number of attributes and increase the accuracy. The performance of the proposed PSO-SVM is compared with the various existing feature selection algorithms such as Info gain, Chi-squared, One attribute based, Consistency subset, Relief, CFS, Filtered subset, Filtered attribute, Gain ratio and PSO algorithm. The SVM classifier is also compared with several classifiers such as Naive Bayes, Random forest and MLP.
Citations
More filters
TL;DR: An IoT framework to evaluate heart disease more accurately using a Modified Deep Convolutional Neural Network (MDCNN) and demonstrates that the proposed MDCNN based heart disease prediction system performs better than other methods.
Abstract: Nowadays, heart disease is the leading cause of death worldwide. Predicting heart disease is a complex task since it requires experience along with advanced knowledge. Internet of Things (IoT) technology has lately been adopted in healthcare systems to collect sensor values for heart disease diagnosis and prediction. Many researchers have focused on the diagnosis of heart disease, yet the accuracy of the diagnosis results is low. To address this issue, an IoT framework is proposed to evaluate heart disease more accurately using a Modified Deep Convolutional Neural Network (MDCNN). The smartwatch and heart monitor device that is attached to the patient monitors the blood pressure and electrocardiogram (ECG). The MDCNN is utilized for classifying the received sensor data into normal and abnormal. The performance of the system is analyzed by comparing the proposed MDCNN with existing deep learning neural networks and logistic regression. The results demonstrate that the proposed MDCNN based heart disease prediction system performs better than other methods. The proposed method shows that for the maximum number of records, the MDCNN achieves an accuracy of 98.2 which is better than existing classifiers.
145 citations
Cites background from "A Machine Learning Framework for Fe..."
...Vijayashree and Sultana [22] introduced a function for recognizing the optimum weights based on population diversity and tuning functions....
[...]
...[22] J. Vijayashree and H. P. Sultana, ‘‘A machine learning framework for feature selection in heart disease classification using improved particle swarm optimization with support vector machine classifier,’’ Program....
[...]
TL;DR: An IoMT framework for the diagnosis of heart disease using modified salp swarm optimization (MSSO) and an adaptive neuro-fuzzy inference system (ANFIS) is proposed, which improves the search capability using the Levy flight algorithm and achieves better accuracy than other approaches.
Abstract: The IoT has applications in many areas such as manufacturing, healthcare, and agriculture, to name a few Recently, wearable devices have become popular with wide applications in the health monitoring system which has stimulated the growth of the Internet of Medical Things (IoMT) The IoMT has an important role to play in reducing the mortality rate by the early detection of disease The prediction of heart disease is a key issue in the analysis of clinical dataset The aim of the proposed investigation is to identify the key characteristics of heart disease prediction using machine learning techniques Many studies have focused on heart disease diagnosis, but the accuracy of the findings is low Therefore, to improve prediction accuracy, an IoMT framework for the diagnosis of heart disease using modified salp swarm optimization (MSSO) and an adaptive neuro-fuzzy inference system (ANFIS) is proposed The proposed MSSO-ANFIS improves the search capability using the Levy flight algorithm The regular learning process in ANFIS is dependent on gradient-based learning and has a tendency to become trapped in local minima The learning parameters are optimized utilizing MSSO to provide better results for ANFIS The following information is taken from medical records to predict the risk of heart disease: blood pressure (BP), age, sex, chest pain, cholesterol, blood sugar, etc The heart condition is identified by classifying the received sensor data using MSSO-ANFIS A simulation and analysis is conducted to show that MSSA-ANFIS works well in relation to disease prediction The results of the simulation demonstrate that the MSSO-ANFIS prediction model achieves better accuracy than the other approaches The proposed MSSO-ANFIS prediction model obtains an accuracy of 9945 with a precision of 9654, which is higher than the other approaches
127 citations
Cites background from "A Machine Learning Framework for Fe..."
...Thus, heart disease is a significant health issue [6]....
[...]
TL;DR: The overall performance is analyzed by comparing the proposed improved ECC with existing Rivest–Shamir–Adleman (RSA)and ECC algorithms.
Abstract: Mobile users are increasing exponentially to adopt ubiquitous services offered by various sectors. This has attracted attention for a secure communication framework to access e-health data on mobile devices. The wearable sensor device is attached to the patient's body which monitors the blood pressure, body temperature, serum cholesterol, glucose level, etc. In the proposed secure framework, first, the task starts with the patient authentication, after that the sensors device linked to the patient is activated and the sensor values of the patient are transmitted to the cloud server. The patient's biometrics information has been added as a parameter in addition to the user name and password. The authentication scheme is coined with the SHA-512 algorithm that ensures integrity. To securely send the sensor information, the method follows two kinds of encryption: Substitution-Ceaser cipher and improved Elliptical Curve Cryptography (IECC). Whereas in improved ECC, an additional key (secret key) is generated to enhance the system's security. In this way, the intricacy of the two phases is augmented. The computational cost of the scheme in the proposed framework is 4H + Ec + Dc which is less than the existing schemes. The average correlation coefficient value is about 0.045 which is close to zero shows the strength of the algorithm. The obtained encryption and decryption time are 1.032 μs and 1.004μs respectively. The overall performance is analyzed by comparing the proposed improved ECC with existing Rivest-Shamir-Adleman (RSA)and ECC algorithms.
87 citations
Islamic Azad University1, Deakin University2, Isfahan University of Technology3, Dibrugarh University4, Shahrekord University5, Ferdowsi University of Mashhad6, University of Southern Queensland7, Polish Academy of Sciences8, Asia University (Taiwan)9, Ngee Ann Polytechnic10, National University of Singapore11, Isfahan University of Medical Sciences12, University of British Columbia13
TL;DR: The experimental results confirm the effectiveness of the proposed feature selection algorithm as compared to the existing state-of-the-art techniques which yielded outstanding results for the development of automated CAD systems.
76 citations
TL;DR: A machine intelligence framework for heart disease diagnosis MIFH has been proposed that returns best possible solution among all input predictive models considering performance criteria and improves the efficacy of the system, hence can assist doctors and radiologists in a better way to diagnose heart patients.
Abstract: Cardiovascular disease tops the list among all major causes of deaths worldwide. Though, prognostication and in-time diagnosis can help in reducing the mortality rate as well as increases the survival rate of patients. Unavailability or scarcity of radiologists and doctors in different countries due to several reasons is a significant factor for hindrance in early diagnosis. Among various efforts of developing the decision support systems, computational intelligence is an emerging trend in the field of medical imaging to detect, prognosticate and diagnose the disease. It helps radiologists and doctors to get relief from being over-burdened and minimizes the induced delays for in-time diagnosis of patients. In this work, a machine intelligence framework for heart disease diagnosis MIFH has been proposed. MIFH utilizes the factor analysis of mixed data (FAMD) to extract as well as derive features from the UCI heart disease Cleveland dataset and train the machine learning predictive models. The framework MIFH is validated using the holdout validation scheme. Experimentation results show that MIFH performed well over several baseline methods of recent times in terms of accuracy and comparable in terms of sensitivity and specificity. MIFH returns best possible solution among all input predictive models considering performance criteria and improves the efficacy of the system, hence can assist doctors and radiologists in a better way to diagnose heart patients.
60 citations
Cites methods from "A Machine Learning Framework for Fe..."
...Vijayashree and Sultana [19] proposed heart disease classification method which collaboratively utilizes the PSO with SVM and reported the classification accuracy 84.36%....
[...]
...[19] J. Vijayashree and H. P. Sultana, ‘‘A machine learning framework for feature selection in heart disease classification using improved particle swarm optimization with support vector machine classifier,’’ Program....
[...]
...Vijayashree and Sultana [19] proposed heart disease classification method which collaboratively utilizes the PSO with SVM and reported the classification accuracy 84....
[...]
...The performance of proposed framework for heart disease diagnosis,MIFH, is compared with several baseline methods recently proposed and came into existence by the academicians and researchers to contribute in developing the decision support system for heart disease diagnosis [7], [19]–[22], [25], [27]....
[...]
References
More filters
TL;DR: A methodology which uses SAS base software 9.1.3 for diagnosing of the heart disease using a neural networks ensemble method, which creates new models by combining the posterior probabilities or the predicted values from multiple predecessor models.
Abstract: In the last decades, several tools and various methodologies have been proposed by the researchers for developing effective medical decision support systems. Moreover, new methodologies and new tools are continued to develop and represent day by day. Diagnosing of the heart disease is one of the important issue and many researchers investigated to develop intelligent medical decision support systems to improve the ability of the physicians. In this paper, we introduce a methodology which uses SAS base software 9.1.3 for diagnosing of the heart disease. A neural networks ensemble method is in the centre of the proposed system. This ensemble based methods creates new models by combining the posterior probabilities or the predicted values from multiple predecessor models. So, more effective models can be created. We performed experiments with the proposed tool. We obtained 89.01% classification accuracy from the experiments made on the data taken from Cleveland heart disease database. We also obtained 80.95% and 95.91% sensitivity and specificity values, respectively, in heart disease diagnosis.
508 citations
TL;DR: A new method for classification of data of a medical database is presented and one of the best results compared with results obtained from related previous studies and reported in the UCI web sites is observed.
Abstract: Data can be classified according to their properties. Classification is implemented by developing a model with existing records by using sample data. One of the aims of classification is to increase the reliability of the results obtained from the data. Fuzzy and crisp values are used together in medical data. Regarding to this, a new method is presented for classification of data of a medical database in this study. Also a hybrid neural network that includes artificial neural network (ANN) and fuzzy neural network (FNN) was developed. Two real-time problem data were investigated for determining the applicability of the proposed method. The data were obtained from the University of California at Irvine (UCI) machine learning repository. The datasets are Pima Indians diabetes and Cleveland heart disease. In order to evaluate the performance of the proposed method accuracy, sensitivity and specificity performance measures that are used commonly in medical classification studies were used. The classification accuracies of these datasets were obtained by k-fold cross-validation. The proposed method achieved accuracy values 84.24% and 86.8% for Pima Indians diabetes dataset and Cleveland heart disease dataset, respectively. It has been observed that these results are one of the best results compared with results obtained from related previous studies and reported in the UCI web sites.
365 citations
TL;DR: MLP was found the best technique to predict presence of CAD in this data set, given its good classificatory performance.
Abstract: In this study, performances of classification techniques were compared in order to predict the presence of coronary artery disease (CAD). A retrospective analysis was performed in 1245 subjects (865 presence of CAD and 380 absence of CAD). We compared performances of logistic regression (LR), classification and regression tree (CART), multi-layer perceptron (MLP), radial basis function (RBF), and self-organizing feature maps (SOFM). Predictor variables were age, sex, family history of CAD, smoking status, diabetes mellitus, systemic hypertension, hypercholesterolemia, and body mass index (BMI). Performances of classification techniques were compared using ROC curve, Hierarchical Cluster Analysis (HCA), and Multidimensional Scaling (MDS). Areas under the ROC curves are 0.783, 0.753, 0.745, 0.721, and 0.675, respectively for MLP, LR, CART, RBF, and SOFM. MLP was found the best technique to predict presence of CAD in this data set, given its good classificatory performance. MLP, CART, LR, and RBF performed better than SOFM in predicting CAD in according to HCA and MDS.
358 citations
TL;DR: This work proposes a highly accurate hybrid method for the diagnosis of coronary artery disease that is able to increase the performance of neural network by approximately 10% through enhancing its initial weights using genetic algorithm which suggests better weights for neural network.
343 citations
TL;DR: It is seen that factors such as chest pain being asymptomatic and the presence of exercise-induced angina indicate the likely existence of heart disease for both men and women, and resting ECG status is a key distinct factor for heart disease prediction.
Abstract: This paper investigates the sick and healthy factors which contribute to heart disease for males and females. Association rule mining, a computational intelligence approach, is used to identify these factors and the UCI Cleveland dataset, a biological database, is considered along with the three rule generation algorithms - Apriori, Predictive Apriori and Tertius. Analyzing the information available on sick and healthy individuals and taking confidence as an indicator, females are seen to have less chance of coronary heart disease then males. Also, the attributes indicating healthy and sick conditions were identified. It is seen that factors such as chest pain being asymptomatic and the presence of exercise-induced angina indicate the likely existence of heart disease for both men and women. However, resting ECG being either normal or hyper and slope being flat are potential high risk factors for women only. For men, on the other hand, only a single rule expressing resting ECG being hyper was shown to be a significant factor. This means, for women, resting ECG status is a key distinct factor for heart disease prediction. Comparing the healthy status of men and women, slope being up, number of coloured vessels being zero, and oldpeak being less than or equal to 0.56 indicate a healthy status for both genders.
329 citations