Machine learning based decision support systems (DSS) for heart disease diagnosis: a review

2017 ◽  
Vol 50 (4) ◽  
pp. 597-623 ◽  
Author(s):  
Saima Safdar ◽  
Saad Zafar ◽  
Nadeem Zafar ◽  
Naurin Farooq Khan
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Disease Diagnosis ◽  
Heart Disease Diagnosis

Review of classification studies for machine learning in the development of intelligent management decision support systems

2020 ◽  
Vol 89 ◽  
pp. 20-29
Author(s):  
Sh. K. Kadiev ◽  
◽  
R. Sh. Khabibulin ◽  
P. P. Godlevskiy ◽  
V. L. Semikov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Mathematical Models ◽  
Decision Support Systems ◽  
Support Systems ◽  
Management Decision ◽  
Classification Methods ◽  
Advantages And Disadvantages ◽  
Intelligent Management ◽  
Management Decision Support

Introduction. An overview of research in the field of classification as a method of machine learning is given. Articles containing mathematical models and algorithms for classification were selected. The use of classification in intelligent management decision support systems in various subject areas is also relevant. Goal and objectives. The purpose of the study is to analyze papers on the classification as a machine learning method. To achieve the objective, it is necessary to solve the following tasks: 1) to identify the most used classification methods in machine learning; 2) to highlight the advantages and disadvantages of each of the selected methods; 3) to analyze the possibility of using classification methods in intelligent systems to support management decisions to solve issues of forecasting, prevention and elimination of emergencies. Methods. To obtain the results, general scientific and special methods of scientific knowledge were used - analysis, synthesis, generalization, as well as the classification method. Results and discussion thereof. According to the results of the analysis, studies with a mathematical formulation and the availability of software developments were identified. The issues of classification in the implementation of machine learning in the development of intelligent decision support systems are considered. Conclusion. The analysis revealed that enough algorithms were used to perform the classification while sorting the acquired knowledge within the subject area. The implementation of an accurate classification is one of the fundamental problems in the development of management decision support systems, including for fire and emergency prevention and response. Timely and effective decision by officials of operational shifts for the disaster management is also relevant. Key words: decision support, analysis, classification, machine learning, algorithm, mathematical models.

A Classification Approach for Heart Disease Diagnosis using Machine Learning

2021 ◽  
Author(s):  
J. Rethna Virgil Jeny ◽  
Nalla Sreeja Reddy ◽  
P. Aishwarya ◽  
Samreen
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Disease Diagnosis ◽  
Classification Approach ◽  
Heart Disease Diagnosis

Benefits and Risks of Machine Learning Decision Support Systems

JAMA ◽  
2017 ◽  
Vol 318 (23) ◽  
pp. 2353 ◽  
Author(s):  
Eta S. Berner ◽  
Bunyamin Ozaydin
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems

scholarly journals Review on Machine Learning Techniques for Diagnosis of Heart Disease

2021 ◽  
Vol 9 (8) ◽  
pp. 1418-1420
Author(s):  
Harshal P. Sabale
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Human Health ◽  
World Health Organisation ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
World Health ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Heart Disease Diagnosis

Abstract: Now-a-days, heart disease is becoming a concern to human health. According to World Health organisation (WHO), heart disease is the number one killer among other fatal diseases. Excessive smoking, alcohol consumption and junk food are culprit for the heart disease. Physical inactivity is also a concerning to the human health. Heart disease is pretty hard to predict or diagnose using traditional methods like counselling. But, now-a-days, medical fields are using machine learning to predict or diagnose different diseases. Implementation of machine learning techniques provides faster and mostly accurate results. This can save many life. In this paper, different machine learning approach for heart disease diagnosis are reviewed. Keywords: Heart disease, CVD, Machine Learning

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