Coronary artery disease detection using a fuzzy-boosting PSO approach

Coronary Artery Disease Classification Using Deep Neural Network and Ensemble Models Optimized by Particle Swarm Optimization

Nowadays, many people are suffering from several health related issues in which Coronary Artery Disease (CAD) is an important one. Identification, prevention and diagnosis of diseases is a very challenging task in the field of medical science. This paper proposes a new feature optimization technique known as PSO-Ensemble1 to reduce the number of features from CAD datasets. The proposed model is based on Particle Swarm Optimization (PSO) with Ensemble1 classifier as the objective function and is compared with other optimization techniques like PSO-CFSE and PSO-J48 with two benchmark CAD datasets. The main objective of this research work is to classify CAD with the proposed PSO-Ensemble1 model using the Ensemble Technique.

A Systematic Review on Prediction Techniques for Cardiac Disease

Mortality rate can be lowered with early prediction of cardiac diseases, which is one of the major issue in healthcare industry. In comparison of traditional methods, intelligent systems have potential to predict these diseases accurately at early stage even with complex data. Various intelligent DSS are presented by researchers for predicting this disease. To study the trends of these intelligent systems, to find the effective techniques for predicting cardiac disease and to find the future directions are the objective of this study. Therefore this paper presents a systematic review on state-of-art techniques based on ML, NN and FL. For analysis, we follow PRISMA statement and considered the studies presented from 2010 to 2020 from different databases. Analysis concluded that ML based techniques are broadly used for feature selection and classification and have the potential for the prediction of cardiac diseases. The future directions are to evaluate the rarely used prediction techniques and finding the way of improving them for model generalization with better prediction accuracy.

A Systematic Literature Review on Particle Swarm Optimization Techniques for Medical Diseases Detection

Artificial Intelligence (AI) is the domain of computer science that focuses on the development of machines that operate like humans. In the field of AI, medical disease detection is an instantly growing domain of research. In the past years, numerous endeavours have been made for the improvements of medical disease detection, because the errors and problems in medical disease detection cause serious wrong medical treatment. Meta-heuristic techniques have been frequently utilized for the detection of medical diseases and promise better accuracy of perception and prediction of diseases in the domain of biomedical. Particle Swarm Optimization (PSO) is a swarm-based intelligent stochastic search technique encouraged from the intrinsic manner of bee swarm during the searching of their food source. Consequently, for the versatility of numerical experimentation, PSO has been mostly applied to address the diverse kinds of optimization problems. However, the PSO techniques are frequently adopted for the detection of diseases but there is still a gap in the comparative survey. This paper presents an insight into the diagnosis of medical diseases in health care using various PSO approaches. This study presents to deliver a systematic literature review of current PSO approaches for knowledge discovery in the field of disease detection. The systematic analysis discloses the potential research areas of PSO strategies as well as the research gaps, although, the main goal is to provide the directions for future enhancement and development in this area. This paper gives a systematic survey of this conceptual model for the advanced research, which has been explored in the specified literature to date. This review comprehends the fundamental concepts, theoretical foundations, and conventional application fields. It is predicted that our study will be beneficial for the researchers to review the PSO algorithms in-depth for disease detection. Several challenges that can be undertaken to move the field forward are discussed according to the current state of the PSO strategies in health care.

Proposing a convolutional neural network for stress assessment by means of derived heart rate from functional near infrared spectroscopy

BACKGROUND

Stress is known as one of the major factors threatening human health. A large number of studies have been performed in order to either assess or relieve stress by analyzing the brain and heart-related signals.

METHOD

In this study, a method based on the Convolutional Neural Network (CNN) approach is proposed to assess stress induced by the Montreal Imaging Stress Task. The proposed model is trained on the heart rate signal derived from functional Near-Infrared Spectroscopy (fNIRS), which is referred to as HRF. In this regard, fNIRS signals of 20 healthy volunteers were recorded using a configuration of 23 channels located on the prefrontal cortex. The proposed deep learning system consists of two main parts where in the first part, the one-dimensional convolutional neural network is employed to build informative activation maps, and then in the second part, a stack of deep fully connected layers is used to predict the stress existence probability. Thereafter, the employed CNN method is compared with the Dense Neural Network, Support Vector Machine, and Random Forest regarding various classification metrics.

RESULTS

Results clearly showed the superiority of CNN over all other methods. Additionally, the trained HRF model significantly outperforms the model trained on the filtered fNIRS signals, where the HRF model could achieve 98.69 ± 0.45% accuracy, which is 10.09% greater than the accuracy obtained by the fNIRS model.

CONCLUSIONS

Employment of the proposed deep learning system trained on the HRF measurements leads to higher stress classification accuracy than the accuracy reported in the existing studies where the same experimental procedure has been done. Besides, the proposed method suggests better stability with lower variation in prediction. Furthermore, its low computational cost opens up the possibility to be applied in real-time monitoring of stress assessment.

Systematic mapping study of data mining–based empirical studies in cardiology

Data mining provides the methodology and technology to transform huge amount of data into useful information for decision making. It is a powerful process to extract knowledge and discover new patterns embedded in large data sets. Data mining has been increasingly used in medicine, particularly in cardiology. In fact, data mining applications can greatly benefits all parts involved in cardiology such as patients, cardiologists and nurses. This article aims to perform a systematic mapping study so as to analyze and synthesize empirical studies on the application of data mining techniques in cardiology. A total of 142 articles published between 2000 and 2015 were therefore selected, studied and analyzed according to the four following criteria: year and channel of publication, research type, medical task and empirical type. The results of this mapping study are discussed and a list of recommendations for researchers and cardiologists is provided.

K, M. M, Roy DS. Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization

With the widespread adoption of e-Healthcare and telemedicine applications, accurate, intelligent disease diagnosis systems have been profoundly coveted. In recent years, numerous individual machine learning-based classifiers have been proposed and tested, and the fact that a single classifier cannot effectively classify and diagnose all diseases has been almost accorded with. This has seen a number of recent research attempts to arrive at a consensus using ensemble classification techniques. In this paper, a hybrid system is proposed to diagnose ailments using optimizing individual classifier parameters for two classifier techniques, namely, support vector machine (SVM) and multilayer perceptron (MLP) technique. We employ three recent evolutionary algorithms to optimize the parameters of the classifiers above, leading to six alternative hybrid disease diagnosis systems, also referred to as hybrid intelligent systems (HISs). Multiple objectives, namely, prediction accuracy, sensitivity, and specificity, have been considered to assess the efficacy of the proposed hybrid systems with existing ones. The proposed model is evaluated on 11 benchmark datasets, and the obtained results demonstrate that our proposed hybrid diagnosis systems perform better in terms of disease prediction accuracy, sensitivity, and specificity. Pertinent statistical tests were carried out to substantiate the efficacy of the obtained results.

A Survey on Using Nature Inspired Computing for Fatal Disease Diagnosis

Genetic Algorithms (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and Artificial Bee Colonies (ABC) are some vital nature inspired computing (NIC) techniques. These approaches have been used in early prophecy of various diseases. This article analyzes the efficacy of various NIC techniques in diagnosing diverse critical human disorders. It is observed that GA, ACO, PSO and ABC have been successfully used in early diagnosis of different diseases. As compared to ACO, PSO and ABC algorithms, GA has been extensively used in diagnosis of ecology, cardiology and endocrinologist. In addition, from the last six years of research, it has been observed that the accuracy accomplished using GA, ACO, PSO and ABC in the early diagnosis of cancer, diabetes and cardio problems lies between 73.5%-99.7%, 70%-99.2%, 80%-98% and 76.4% to 99.98% respectively. Furthermore, ACO, PSO and ABC are found to be best suited in diagnosing lung, prostate and breast cancer respectively. Moreover, the hybrid use of NIC techniques produces better results as compared to their individual use.