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Heart disease is a deadly disease that large population of people around the world suffers from. When considering death rates and large number of people who suffers from heart disease, it is revealed how important early diagnosis of heart disease. Traditional way of diagnosis is not sufficient for such an illness. Developing a medical diagnosis system based on machine learning for prediction of heart...
Chronic heart failure represents a global pandemic, currently affecting over 26 million of patients worldwide. It is a major contributor in the death rate of patients with cardiovascular diseases and results in more than 1 million hospitalizations annually in Europe and North America. Methods for chronic heart failure detection can be utilized to act preventive, improve early diagnosis and avoid hospitalizations...
Heart disease classification is one of the most important topics in clinical decision support systems (CDSS). However, the performance of classification is greatly affected by feature selection. Canonical correlation analysis (CCA) is a popular method to extract effective features from two relevant data sets. In this paper, we employ discriminant minimum class locality preserving canonical correlation...
With the development of machine learning techniques, artificial intelligence applications in medicine are becoming hot topic in health information systems. In this research, we construct a new basic heart failure disease database which contains 1715 patients and 400 features. Then, we propose a new machine learning method called Polynomial Smooth Support Vector Machine(PSSVM) to help doctors diagnose...
Machine learning is the science of getting computers to learn from data without being unambiguously programmed. Instigated two supervised classification problem (Heart disease prediction and Springleaf Marketing Response) using machine learning algorithms. In heart disease prediction, the objective is to envisage the status of heart disease which can be 0, 1, 2, 3 and 4. These 5 classes are not cleared...
Heart disease is one of the diseases which has highest mortality rate recently. Heart's electrical activity examination and interpretation are very important for the understanding of diseases. In this study, electrocardiogram signals are analyzed, then patient's healthy and arrhythmia beats are extracted. RR, QRS, Skewness and Linear Predictive Coding coefficients of the signals are considered for...
Healthcare in simplest form is all about diagnosis and prevention of disease or treatment of any injury by a medical practitioner. It plays an important role in providing quality life for the society. The concern is how to provide better service with less expensive therapeutically equivalent alternatives. Machine Learning techniques (ML) help in achieving this goal. Healthcare has various categories...
We present an automated disease term classification model using machine learning techniques that classifies a medical term to a specific disease class. We work on five particular diseases: Cancer, AIDS, Arthritis, Diabetes and heart related ailments. We identify and classify medical terms like drug names, symptoms, abbreviations, disease names, tests, etc., into their specific diseases classes. The...
In medical field the disease diagnosis is often made based on the knowledge and experience of the medical practitioner. Due to this there are chances of errors, unwanted biases and also takes longer time in accurate diagnosis of disease. In case of heart disease, its diagnosis is most difficult task. It depends on the careful analysis of different clinical and pathological data of the patient by medical...
In medical field the diagnosis of heart disease is most difficult task. It depends on the careful analysis of different clinical and pathological data of the patient by medical experts, which is complicated process. Due to advancement in machine learning and information technology, the researchers and medical practitioners in large extent are interested in the development of automated system for the...
Truly, heart is successor to the brain in being the most significant vital organ in the body of a human. Heart, being a magnificent pump, has his performance orchestrated via a group of valves and highly sophisticated neural control. While the kinetics of the heart is accompanied by sound production, sound waves produced, by the heart, are reliable diagnostic tools to check heart activity. Chronologically,...
Binary classifiers have typically been the norm for building classification models in the Machine Learning community. However, an alternate to binary classification is one-class classification, which aims to build models using only a single class of data. This is particularly useful when there is an over-abundance of data of a particular class. In such imbalanced cases, binary classifiers may not...
Automated detection of heart valve disease through heart sound has a great requirement due to its inexpensive and non-invasive availability. Extensive research has been conducted recently on applying different classification and features selection techniques. Heart sound data sets represent a real life data that contains continuous attributes and a large number of features that could be hardly classified...
This paper describes how to make use of the cost information related to the extraction of each feature in a feature selection algorithm. For instance, in medical diagnosis, the different tests a patient might take during the diagnosis process can have different associated costs. The main idea is to change the feature selection framework in order to get low-cost subsets of informative features. This...
Based on the concept of granular computing, this article proposes a novel Boolean conversion (BC) method to reduce data attribute number for the purpose of improving the efficiency of learning in artificial intelligence. Data with large amount of attributes usually cause a system freezes or shuts down. The proposed method combines large amount attributes to smaller number ones by the way of Boolean...
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