Decision Support System for Black Classification of Dental Images Using GIST Descriptors

2020 ◽  
pp. 343-352 ◽  
Author(s):  
Prerna Singh ◽  
Priti Sehgal
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System

Knowledge Discovery and Data Mining Applications in the Healthcare Industry

2015 ◽  
pp. 241-262
Author(s):  
Iman Barazandeh ◽  
Mohammad Reza Gholamian
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Support System ◽  
Knowledge Discovery ◽  
Support System ◽  
Healthcare Industry ◽  
Actionable Knowledge ◽  
System View ◽  
Data Market

The healthcare industry is one of the most attractive domains to realize the actionable knowledge discovery objectives. This chapter studies recent researches on knowledge discovery and data mining applications in the healthcare industry and proposes a new classification of these applications. Studies show that knowledge discovery and data mining applications in the healthcare industry can be classified to three major classes, namely patient view, market view, and system view. Patient view includes papers that performed pure data mining on healthcare industry data. Market view includes papers that saw the patients as customers. System view includes papers that developed a decision support system. The goal of this classification is identifying research opportunities and gaps for researchers interested in this context.

scholarly journals Clinical Decision Support System (CDSS) for the Classification of Atypical Cells in Pleural Effusions

2013 ◽  
Vol 20 ◽  
pp. 379-384 ◽  
Author(s):  
David Bassen ◽  
Saurabh Nayak ◽  
Xia Chong Li ◽  
Mitchell Sam ◽  
Jagmohan Sidhu ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Pleural Effusions ◽  
Atypical Cells

Cloud‐based decision support system for the detection and classification of malignant cells in breast cancer using breast cytology images

2019 ◽  
Vol 82 (6) ◽  
pp. 775-785 ◽  
Author(s):  
Tanzila Saba ◽  
Sana Ullah Khan ◽  
Naveed Islam ◽  
Naveed Abbas ◽  
Amjad Rehman ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Malignant Cells

DECISION SUPPORT SYSTEM FOR CLASSIFICATION OF NATURAL RISK IN MARITIME CONSTRUCTION

2009 ◽  
Author(s):  
Marco Antonio García Tamargo ◽  
Alfredo S. Alguero García ◽  
Víctor Castro Amigo ◽  
Amelia Bilbao Terol ◽  
Andrés Alonso Quintanilla
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Natural Risk

Decision support system for classification of a finite set of multicriteria alternatives

2002 ◽  
Vol 33 (1) ◽  
pp. 13-21 ◽  
Author(s):  
O.I. Larichev ◽  
A.V. Kortnev ◽  
D.Yu. Kochin
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Finite Set

Development of an Automated Decision Support System for Diagnosis of Digestive Disorders Using Electrogastrograms

2018 ◽  
pp. 97-119 ◽  
Author(s):  
Arivarasu Rajagopal ◽  
Paramasivam Alagumariappan ◽  
Kamalanand Krishnamurthy
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Digestive Tract ◽  
Support System ◽  
Classification System ◽  
Descriptive Statistics ◽  
Automated Classification ◽  
Mode Decomposition ◽  
Digestive Disorders

The disorders of the digestive tract lead to various problems such as bleeding, bloating, nausea, etc. In order to diagnose various digestive abnormalities, the electrogastrograms (EGG) can serve as an efficient tool. In an EGG, several electrodes are placed onto the abdomen over the stomach and the electrical signals originating from the stomach muscles are recorded. By analyzing these electrical patterns, the abnormalities in digestive system can be analyzed. This chapter describes the developed system for measuring EGG signals along with the decision support system developed for automated classification of digestive disorders. The normal and abnormal EGG signals were acquired at Balaji Medical Hospital, Chennai. Further, the features were extracted using descriptive statistics and empirical mode decomposition (EMD) algorithm. Finally, an automated classification system was developed using k-means algorithm. This chapter explains the recording of electrogastrograms and a method for classification of normal and abnormal EGG signals.

Development of an Automated Decision Support System for Diagnosis of Digestive Disorders Using Electrogastrograms

2020 ◽  
pp. 661-678
Author(s):  
Arivarasu Rajagopal ◽  
Paramasivam Alagumariappan ◽  
Kamalanand Krishnamurthy
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Digestive Tract ◽  
Support System ◽  
Classification System ◽  
Descriptive Statistics ◽  
Automated Classification ◽  
Mode Decomposition ◽  
Digestive Disorders

The disorders of the digestive tract lead to various problems such as bleeding, bloating, nausea, etc. In order to diagnose various digestive abnormalities, the electrogastrograms (EGG) can serve as an efficient tool. In an EGG, several electrodes are placed onto the abdomen over the stomach and the electrical signals originating from the stomach muscles are recorded. By analyzing these electrical patterns, the abnormalities in digestive system can be analyzed. This chapter describes the developed system for measuring EGG signals along with the decision support system developed for automated classification of digestive disorders. The normal and abnormal EGG signals were acquired at Balaji Medical Hospital, Chennai. Further, the features were extracted using descriptive statistics and empirical mode decomposition (EMD) algorithm. Finally, an automated classification system was developed using k-means algorithm. This chapter explains the recording of electrogastrograms and a method for classification of normal and abnormal EGG signals.

Knowledge Discovery and Data Mining Applications in the Healthcare Industry

2016 ◽  
pp. 1097-1118 ◽  
Author(s):  
Iman Barazandeh ◽  
Mohammad Reza Gholamian
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Support System ◽  
Knowledge Discovery ◽  
Support System ◽  
Healthcare Industry ◽  
Actionable Knowledge ◽  
System View ◽  
Data Market

The healthcare industry is one of the most attractive domains to realize the actionable knowledge discovery objectives. This chapter studies recent researches on knowledge discovery and data mining applications in the healthcare industry and proposes a new classification of these applications. Studies show that knowledge discovery and data mining applications in the healthcare industry can be classified to three major classes, namely patient view, market view, and system view. Patient view includes papers that performed pure data mining on healthcare industry data. Market view includes papers that saw the patients as customers. System view includes papers that developed a decision support system. The goal of this classification is identifying research opportunities and gaps for researchers interested in this context.

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