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Oscar Camacho-Nieto. The rapid proliferation of connectivity, availability of ubiquitous computing, miniaturization of sensors and communication technology, have changed healthcare in all its areas, creating the well-known healthcare paradigm of e-Health. In this paper, an embedded system capable of monitoring, learning and classifying biometric als is presented. The machine learning model is based on associative memories to predict the presence or absence of coronary artery disease in patients.
Classification accuracy, sensitivity and specificity show that the performance of our proposal exceeds the performance achieved by each of the fifty widely known algorithms against which it was compared.
Keywords: associative memories; decision support systems; e-Health; Internet of Things; pattern classification associative memories ; decision support systems ; e-Health ; Internet of Things ; pattern classification. Public health systems were deed to attend and provide health services to specific individuals. However, the constant growth of population and the increasing costs of health services imply that the public health system will face new challenges [ 3 ].
Some important aspects to consider are to develop and evaluate innovative approaches for improving the quality of healthcare using sensors applications in medical monitoring. Two decades ago, technology researchers took an important role towards improving the medical care of patients through the evolution of the concept of a network of smart devices, which would be known as Wireless Sensor Networks WSNs [ 4 ]. In the same decade, the concept of moving small amounts of data to a large set of nodes evolved to what today is known as the Internet of Things IoT [ 56 ].
The IoT paradigm represents one of the most disruptive technologies, enabling ubiquitous computing scenarios for medical monitoring, and decision making [ 78 ]; creating the well-known healthcare paradigm of e-Health [ 9 ]. This paradigm arises as a result of the combination of emerging technologies, such as IoT, ubiquitous computing, WSNs, high-speed communications infrastructure, and the social need for more effective health services with better accessibility and availability [ 10 ].
The e-Health paradigm has changed the traditional way in which healthcare services are provided. With this paradigm the user of healthcare services does not need to move to medical facilities to carry out a routine follow-up [ 1112 ]; on the contrary, medical monitoring can be carried out from where the patient is located [ 1314 ].
In addition, all acquired data can be transmitted, processed and stored for data mining and decision making by medical specialists [ 1516 ]. Nowadays, it is increasingly common to use applications based on artificial intelligence techniques to support medical specialists in decision making.
For more than a decade, statistical techniques [ 17 ], expert systems [ 18 ], neural networks [ 19 ], decision trees [ 20 ] and associative memories [ 2122 ] have been widely used for pattern recognition, feature selection, data mining and decision making in the medical field [ 232425 ]. The paper is organized as follows.
Section 2 presents works related to machine learning based systems that have been applied to predict the presence or absence of coronary artery disease in patients. In Section 3a succinct description of associative memories fundamentals is presented. Section 5 presents the three main performance indicators of a binary classification test. The experimental phase is described in Section 6. In Section 7sensitivity, specificity and classification accuracy achieved by each of the compared algorithms in two datasets related to coronary artery disease diagnosis, are presented.
For more than a decade, machine learning based systems have been tested in patients with cardiac disease to predict outcome, or in the general population to detect cardiac diseases. InKahramanli and Allahverdi [ 26 ] proposed a hybrid neural system for heart diseases that includes artificial neural network ANN and fuzzy neural network FNN ; the dataset was obtained from the University of California at Irvine UCI machine learning repository [ 27 ].
InMcSherry [ 29 ] proposed an approach to conversational case-based reasoning CCBR in medical classification and diagnosis that aims to increase transparency while also providing high levels of accuracy and efficiency; two datasets from the UCI machine learning repository were used in the experimental phase. This work focuses on the use of classical associative memories for medical patterns classification.
This approach incorporates a learning reinforcement stage, which increases the classification performance of classical models of associative memories. The performance was validated on medical datasets collected from the UCI machine learning repository. InAnooj [ 30 ] proposed a weighted fuzzy rule-based clinical decision support system CDSS for the diagnosis of coronary artery disease; the experimentation was carried out on the proposed system using the datasets obtained from the UCI machine learning repository and the performance of the system was compared with a neural network-based system utilizing accuracy, sensitivity and specificity.
InNahar et al. InBiswas et al. The performance was validated on coronary artery disease dataset collected from the UCI machine learning repository. In this work, Delta Associative Memory was presented. The operation of this model is based on the differences that exist between patterns of different classes and a dynamic threshold that is calculated for each unknown pattern to be classified. The experimental were competitive, when compared against algorithms in the current literature. InNguyen et al. The proposed method was evaluated using Cleveland coronary artery disease dataset from the UCI machine learning repository.
InLeema et al. InNahato et al. The three major subsystems in the FELM framework are preprocessing subsystem, fuzzification subsystem and classification subsystem. Missing value imputation and outlier elimination are handled by the preprocessing subsystem. Cleveland coronary artery disease dataset from the UCI machine learning repository was used for experimentation.
This associative model overcome the limitations of the original Alpha-Beta Associative Memories [ 36 ]. InShah et al. Methodology performance was evaluated through accuracy, specificity and sensitivity over the three datasets of the UCI machine learning repository. The first models of Associative Memories arise with the scientific findings of Steinbuch in the s [ 383940 ], which over time would be known as Learning Matrices. In any learning matrix, there are two phases that determine the performance of each model, namely learning phase and classification phase.
Learning matrices are structures formed by rows and columns whose intersection points are formed by connecting elements [ 41 ]. The characteristics of an object are presented during the learning phase to the columns as binary als via a suitable transducer. Simultaneously, a meaning of an object associated with this set of characteristics is applied in the form of a al to one of the rows. Therefore, so-called conditioned connections are effected in the connective elements of the row selected by the meaning [ 42 ].
Generalizing, a conditioned connection is a functional connection between a row and a column.
Thus, an associative memory M is generated from an a priori finite set of known associations, called the fundamental set of associations. Associative memories have been widely used to perform pattern recognition tasks effectively, however, they present a limitation known as cross-talk. The influence of cross-talk causes the associative memory to become saturated and consequently the classification performance is negatively affected.
This modification consists of adding a data preprocessing stage before the Delta Associative Memory learning phase. In the present paper, those concepts are used but with very different purposes, namely: to obtain a transformed fundamental set of patterns. The details of Delta Associative Memory model can be reviewed in Reference [ 24 ]. It should be noted that both the learning phase and the classification phase of Delta Associative Memory model remained unchanged.
For clarity purposes, in the present paper, the same symbology is used. Data preprocessing phase is applied before Delta Associative Memory learning phase. This phase transforms the values of the input patterns of the fundamental set. This transformation of the input patterns is a data translation process that does not affect its representation or its statistical distribution.Free text sex Steinbuch
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