| Autore |
Acharjya D. P
|
| Edizione | [1st ed.] |
| Pubbl/distr/stampa |
Singapore : , : Springer Singapore Pte. Limited, , 2024
|
| Descrizione fisica |
1 online resource (401 pages)
|
| Altri autori (Persone) |
MaKun
|
| Collana |
Studies in Computational Intelligence Series
|
| ISBN |
981-9988-53-5
|
| Formato |
Materiale a stampa  |
| Livello bibliografico |
Monografia |
| Lingua di pubblicazione |
eng
|
| Nota di contenuto |
Intro -- Preface -- Acknowledgments -- Contents -- Editors and Contributors -- Acronyms -- Theoretical Foundation of Computational Intelligence Techniques -- Refining Metabolic Network by Fuzzy Matching of Metabolite Names for Improving Metabolites Ranking Toward the Diseases -- 1 Introduction -- 2 Literature Survey -- 3 Materials and Methods -- 3.1 Pairwise Disease Similarity -- 3.2 Threshold Matching Based Metabolite Name Mapping -- 3.3 Fuzzy Matching Based Metabolite Name Matching Algorithm -- 3.4 Pairwise Metabolite Similarity -- 3.5 Identification and Ranking of Disease-Related Metabolites -- 4 Result Analysis -- 4.1 Performance Measures of Threshold and Fuzzy Matching -- 5 Conclusion -- References -- Learning from Imbalanced Data in Healthcare: State-of-the-Art and Research Challenges -- 1 Introduction -- 2 Literature Review of Imbalance Healthcare Data -- 2.1 Imbalanced Cancer Data Diagnosis -- 2.2 Prediction of Imbalanced Covid-19 Data -- 2.3 Drug Prediction with Imbalanced Data -- 2.4 Imbalance Classification of Diabetes -- 2.5 Rare Disease Prediction with Imbalanced Data -- 2.6 Depression and Suicidal Detection with Imbalanced Data -- 3 Methodologies for Handling Imbalance Data in Healthcare -- 3.1 Algorithm Approach -- 3.2 Data-Level Approach -- 3.3 Cost-Sensitive Approach -- 3.4 Multiple Classifier Ensemble Approach -- 4 Source of Imbalanced Healthcare Data -- 5 Conclusions and Future Aspects -- References -- A Review on Metaheuristic Approaches for Optimization Problems -- 1 Introduction -- 2 Metaheuristic Approach and Types -- 2.1 Swarm Intelligence Algorithms -- 2.2 Evolutionary Algorithm -- 2.3 Bio Inspired Algorithms -- 2.4 Physics and Chemistry Based Algorithms -- 2.5 Other Algorithms -- 3 Category Wise Representatives of Metaheuristic Approaches -- 3.1 Cuckoo Search Algorithm -- 3.2 Genetic Algorithm -- 3.3 Harmony Search.
3.4 Biogeography Based Optimization -- 4 Conclusion and Future Scope -- References -- Diabetes Prediction: A Comparison Between Generalized Linear Model and Machine Learning -- 1 Introduction -- 2 Data Mining Process -- 2.1 Classification -- 2.2 Types of Classification Techniques -- 2.3 Major Classification Algorithms -- 3 Related Research Work -- 4 Computational Methodology -- 4.1 Data Pre-processing -- 4.2 Application of Classification Techniques -- 5 Experimental Results and Discussion -- 5.1 Binary Logistics Regression -- 5.2 Support Vector Machine -- 6 Conclusion -- References -- Prediabetes Prediction Using Response Surface Methodology and Probabilistic Neural Networks Model in an Ethnic South Indian Population -- 1 Introduction -- 2 Brief Introduction to Prediabetes -- 3 Materials and Methods -- 3.1 Clinical Study -- 3.2 Biochemical Study -- 4 Computational Intelligence Techniques in Prediabetes Prediction -- 4.1 Pearson Correlation -- 4.2 Response Surface Methodology -- 4.3 Artificial Neural Network -- 4.4 Probabilistic Neural Networks -- 5 Results and Analysis -- 5.1 Pearson Correlation Analysis -- 5.2 Response Surface Methodology Analysis -- 5.3 Artificial Neural Networks -- 5.4 Probabilistic Neural Networks -- 6 Discussion -- 6.1 Residual Plots for Predicted Glycemic Levels Using RSM -- 6.2 Regression Plot for Prediabetes Prediction Using ANN -- 7 Conclusion -- References -- Localization and Classification of Brain Tumor Using Multi-layer Perceptron -- 1 Introduction -- 2 Background Literature -- 3 Foundations of Neural Network -- 3.1 Feed-Forward Networks -- 3.2 Recurrent Networks -- 3.3 Radial Basis Network -- 3.4 Multi-layer Perceptron -- 4 Phases of Brain Tumor Detection -- 5 Experimental Results -- 6 Conclusion -- References -- Computational Intelligence in Analyzing Health Data.
Information Retrieval from Healthcare Information System -- 1 Introduction -- 2 Challenges of Information Retrieval in Health Care -- 2.1 The Medical Healthcare Benefits and Challenges -- 2.2 IoT-Based Medical Services Data Model -- 2.3 Frequently Attainable Metadata Model for IoT Data -- 3 UDA-IoT Ubiquitous Data Accessing for Information System -- 3.1 Accessing UDA-IoT Data and Cloud Platform -- 4 A Case Study on UDA-IoT Methodology -- 4.1 Emergency Medical DSS Ubiquitous Data Accessing Implementation -- 4.2 Discussion -- 5 Conclusion -- References -- Association Rule Mining for Healthcare Data Analysis -- 1 Introduction -- 2 Related Works -- 2.1 Liver Diseases -- 2.2 Heart Diseases -- 2.3 Kidney Diseases -- 3 Association Rule Mining -- 4 Measures Used in Association Rule Mining -- 5 Experimental Analysis and Results -- 6 Conclusion and Future Direction -- References -- Feature Selection and Classification of Microarray Cancer Information System: Review and Challenges -- 1 Introduction -- 2 Background Study -- 2.1 Fundamentals of Feature Selection -- 2.2 Fundamental Classification Techniques -- 3 Related Research Work -- 4 Result and Analysis -- 4.1 Analysis Based on Feature Selection -- 4.2 Analysis Based on Dataset -- 4.3 Analysis Based on Classifier -- 5 Conclusion -- References -- Early Detection of Osteoporosis and Osteopenia Disease Using Computational Intelligence Techniques -- 1 Introduction -- 2 Methods of Computational Intelligence -- 2.1 Artificial Neural Networks for Osteoporosis Classification -- 2.2 Extreme Learning Machine in Osteoporosis Classification -- 3 A General Evaluation Scheme with a Block Diagram -- 4 Findings and Evaluation -- 5 Conclusion -- References -- Pathway to Detect Cancer Tumor by Genetic Mutation -- 1 Introduction -- 2 Background Study -- 2.1 Literature Survey -- 3 System Modeling -- 4 Materials and Methods.
4.1 Stacking Model -- 4.2 K-Nearest Neighbor -- 4.3 Linear Support Vector Machines -- 5 Experiment and Result Analysis -- 5.1 Dataset -- 5.2 Performance Analysis -- 5.3 Machine Learning Model Implementations -- 5.4 Comparison of Machine Learning Models -- 6 Conclusion and Future Scope -- References -- A Knowledge Perception: Physician and Patient Toward Telehealth in COVID-19 -- 1 Introduction -- 2 Review of Telehealth and Telemedicine Services -- 3 Methodology -- 4 Results Analysis -- 4.1 Patient's Perception of Telehealth -- 4.2 Physician's Perception of Telehealth -- 4.3 Data Interpretation on Patient's Response -- 4.4 Data Interpretation on Physician's Response -- 5 Conclusion -- References -- Computational Intelligence in Electronic Health Record -- Classification of Cardiovascular Disease Information System Using Machine Learning Approaches -- 1 Introduction -- 2 Machine Learning for Cardiovascular Disease Classification -- 3 Cardiovascular Disease Information System -- 4 Exploratory Data Analysis -- 5 Performance Measures -- 6 Conclusion -- References -- Automatic Edge Detection Model of MR Images Based on Deep Learning Approach -- 1 Introduction -- 2 Materials and Methods -- 2.1 Fuzzy Logic Approach -- 2.2 Neuro-Fuzzy Approach -- 3 Proposed Research Design Workflow -- 4 Experimental Results and Analysis -- 5 Conclusions -- References -- Lung Disease Classification Based on Lung Sounds-A Review -- 1 Introduction -- 2 Natural Ways to Recognize Symptoms -- 3 Clinical Process to Recognize Pneumonia -- 4 Data Availability -- 5 Computational Intelligence in Lung Sound Classification -- 5.1 Feature Extraction Methods and the Classification -- 5.2 Miscellaneous Methods -- 6 Conclusion -- References -- Analysis of Forecasting Models of Pandemic Outbreak for the Districts of Tamil Nadu -- 1 Introduction -- 2 Literature Survey.
3 Research Methodology -- 3.1 SIR Model -- 3.2 ARIMA Model -- 3.3 Forecasting -- 4 Results and Discussions -- 5 Conclusion -- References -- Suppression of Artifacts from EEG Recordings Using Computational Intelligence -- 1 Introduction -- 2 Computational Intelligence -- 2.1 Evolutionary Computing -- 2.2 Swarm Intelligence -- 3 Characteristics of the EEG Signal -- 3.1 Types of Artifacts -- 4 Artifact Removal Techniques -- 4.1 Filtering Methods -- 4.2 Regression Methods -- 4.3 Wavelet Transform -- 4.4 Blind Source Separation -- 4.5 Mode Decomposition Methods -- 5 Performance Evaluation and Discussion -- 6 Conclusion -- References -- Rough Computing in Healthcare Informatics -- 1 Introduction -- 2 Information System -- 3 Rough Computing -- 3.1 Rough Set -- 3.2 Fuzzy Rough Set -- 3.3 Rough Set on Fuzzy Approximation Space -- 3.4 Rough Set on Intuitionistic Fuzzy Approximation Space -- 4 Hybridized Rough Computing -- 5 Healthcare Informatics -- 5.1 Feature Selection -- 5.2 Classification -- 5.3 Clustering -- 5.4 Decision Support System -- 6 Healthcare Applications -- 7 Conclusion -- References -- Computational Intelligence in Ethical Issues in Healthcare -- Ethical Issues on Drug Delivery and Its Impact in Healthcare -- 1 Introduction -- 2 Review of Literature -- 3 Rudiments of Genetic Algorithm -- 3.1 Fitness Function -- 3.2 Selection -- 3.3 Crossover -- 3.4 Mutation -- 4 Problem Formulation -- 4.1 Modeling of the Problem -- 5 Methodology -- 5.1 Complete Elitist Genetic Algorithm -- 6 Results and Discussions -- 6.1 Experimental Results -- 6.2 Analysis of the Findings -- 6.3 Comparative Study -- 7 Conclusion and Future Extensions -- References -- Privacy-Preserving Deep Learning Models for Analysis of Patient Data in Cloud Environment -- 1 Introduction -- 2 Medical Data, Deep Learning, and Cloud Computing -- 2.1 Medical Data and Secondary Usage.
2.2 Deep Learning.
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| Record Nr. | UNINA-9910841858203321 |
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Computational Intelligence in Healthcare Informatics
