Development of Cardiac Disease Classifier Using Rough Set Decision System
01 Jan 2019-pp 775-785
TL;DR: The rough set-based degree of attributes dependency technique and their significance predicted the universal least decision rules, which has the least number of attributes so that their combination defines the largest subset of a universal decision class.
Abstract: This paper describes the rough set classifier for cardiac disease classification over the medical dataset obtained from characteristics feature of ECG signals. The sets of characterizes feature are used as an information system to find minimal decision rules that may be used to identify one or more diagnostic classes. After gathering knowledge from various medical books as well as feedback from well-known cardiologists, a knowledge base has been developed. The rough set-based degree of attributes dependency technique and their significance predicted the universal least decision rules. Such rule has the least number of attributes so that their combination defines the largest subset of a universal decision class. Hence, the minimal rule of an information system is adequate for predicting probable complications. Lastly, the performance parameters such as accuracy and sensitivity have been expressed in the form of confusion matrix by ROSETTA software which yields information about actual and predicted classification achieved by the proposed system.
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TL;DR: A computerized diagnosis system is developed using Rough set classifier from multi-lead ECG signal for detection as well as the classification of five different types of myocardial infarction (MI) disease, which illustrates its outperformance over the existing approaches in terms of sensitivity, accuracy, and sensitivity.
Abstract: In this study, a computerized diagnosis system is developed using Rough set classifier from multi-lead ECG signal for detection as well as the classification of five different types of myocardial i
17 citations
TL;DR: This paper illustrates the cloud-based telemonitoring framework that implements healthcare automation system for myocardial infarction (MI) disease classification that reduces both data storage space and transmission bandwidth which facilitates accessibility to quality care in much reduced cost.
Abstract: This paper illustrates the cloud-based telemonitoring framework that implements healthcare automation system for myocardial infarction (MI) disease classification. For this purpose, the pathological feature of ECG signal such as elevated ST segment, inverted T wave, and pathological Q wave are extracted, and MI disease is detected by the rule-based rough set classifier. The information system involves pathological feature as an attribute and decision class. The degree of attributes dependency finds a smaller set of attributes and predicted the comprehensive decision rules. For MI decision, the ECG signal is shared with the respective cardiologist who analyses and prescribes the required medication to the first-aid professional through the cloud. The first-aid professional is notified accordingly to attend the patient immediately. To avoid the identity crisis, ECG signal is being watermarked and uploaded to the cloud in a compressed form. The proposed system reduces both data storage space and transmission bandwidth which facilitates accessibility to quality care in much reduced cost.
4 citations
TL;DR: In this article , the Rough Set algorithm was used to classify public interest in the Covid-19 vaccine, which resulted in 3 reductions with 58 rules based on 100 respondents, while the target attribute is the result that contains the community's interest or not to participate in vaccination.
Abstract: Rough Set is a machine learning algorithm that analyses and determines important attributes based on an uncertain data set. The purpose of this study is to classify public interest in the Covid-19 vaccine. Vaccination is one of the solutions from the government that is considered the most appropriate to reduce the number of Covid-19 cases. Data collection was taken through a questionnaire distributed to the village community in Air Manik Village, Padang-West Sumatra, randomly as many as 100 respondents. The assessment attributes in this study are Vaccine Understanding (1), Environment (2), Community Education (3), Vaccine Confidence (4), and Cost (5), while the target attribute is the result that contains the community’s interest or not to participate in vaccination. The analysis process is assisted using the Rosetta application. This study resulted in 3 reductions with 58 rules based on 100 respondents. This study concludes that the Rough Set algorithm can be used to classify public interest in the Covid-19 vaccine. Based on this research, it is hoped that it can provide information and input for local governments to be more aggressive in urging and encouraging the public to be vaccinated.
References
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TL;DR: This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.
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TL;DR: A new approach to ECG arrhythmia analysis is described, based on hidden Markov modeling (HMM), a technique successfully used since the mid 1970s to model speech waveforms for automatic speech recognition.
Abstract: A new approach to ECG arrhythmia analysis is described. It is based on hidden Markov modeling (HMM), a technique successfully used since the mid 1970s to model speech waveforms for automatic speech recognition. Many ventricular arrhythmias can be classified by detecting and analyzing QRS complexes and determining R-R intervals. Classification of supraventricular arrhythmias, however, often requires detection of the P wave in addition to the QRS complex. The HMM approach combines structural and statistical knowledge of the ECG signal in a single parametric model. Model parameters are estimated from training data using an iterative, maximum-likelihood reestimation algorithm. Initial results suggest that this approach can provide improved supraventricular arrhythmia analysis through accurate representation of the entire beat, including the P-wave. >
527 citations