Machine learning for healthcare systems: foundations and applications
This book provides various insights into machine learning techniques in healthcare system data and its analysis
Gespeichert in:
| Format: | Elektronisch E-Book |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Gistrup, Denmark
River Publishers
[2023]
|
| Schriftenreihe: | River Publishers Series in Computing and Information Science and Technology
|
| Schlagworte: | |
| Online-Zugang: | FHI01 Volltext https://public.ebookcentral.proquest.com/choice/PublicFullRecord.aspx?p=7275994 Taylor & Francis |
| Zusammenfassung: | This book provides various insights into machine learning techniques in healthcare system data and its analysis |
| Beschreibung: | Description based upon print version of record |
| Beschreibung: | 1 Online-Ressource (xxvii, 222 Seiten) Diagramme |
| ISBN: | 9781000959994 1000959996 8770228116 9788770228114 9781000959987 1000959988 9788770228107 8770228108 9781003438816 1003438814 |
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| 245 | 1 | 0 | |a Machine learning for healthcare systems |b foundations and applications |
| 264 | 1 | |a Gistrup, Denmark |b River Publishers |c [2023] | |
| 300 | |a 1 Online-Ressource (xxvii, 222 Seiten) |b Diagramme | ||
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| 338 | |b cr |2 rdacarrier | ||
| 490 | 0 | |a River Publishers Series in Computing and Information Science and Technology | |
| 500 | |a Description based upon print version of record | ||
| 505 | 8 | |a Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- List of Contributors -- List of Figures -- List of Tables -- List of Abbreviations -- Chapter 1: Investigation on Improving the Performance of Class-imbalanced Medical Health Datasets -- 1.1: Introduction -- 1.2: Problem Formulation -- 1.3: Techniques to Handle Imbalanced Datasets -- 1.3.1: Random undersampling (RUS) -- 1.3.2: Random oversampling (RUS) -- 1.3.3: Synthetic minority oversampling technique (SMOTE) -- 1.4: Classification Models -- 1.4.1: Naive Bayes | |
| 505 | 8 | |a 1.4.2: k-Nearest neighbor classifier -- 1.4.3: Decision tree classifier -- 1.4.4: Random forest -- 1.5: Dataset Collection -- 1.5.1: Heart failure clinical records dataset -- 1.5.2: Diabetes dataset -- 1.6: Experimental Results and Discussion -- 1.6.1: Heart failure clinical records dataset -- 1.6.2: Diabetes dataset -- 1.7: Conclusion -- References -- Chapter 2: Improving Heart Disease Diagnosis using Modified Dynamic Adaptive PSO (MDAPSO) -- 2.1: Introduction -- 2.2: Background and Related Work -- 2.2.1: PSO modification -- 2.2.2: Feature selection -- 2.2.3: Classification and clustering | |
| 505 | 8 | |a 2.3: Proposed Approach -- 2.3.1: PSO algorithm -- 2.3.2: Inertia weight -- 2.3.3: Modified dynamic adaptive particle swarm optimization (MDAPSO) -- 2.4: Experimental Setup and Results -- 2.4.1: Dataset -- 2.4.2: Performance metrics -- 2.4.3: Result -- 2.5: Conclusion -- References -- Chapter 3: Efficient Diagnosis and ICU Patient Monitoring Model -- 3.1: Introduction -- 3.2: Main Text -- 3.2.1: Disease prediction -- 3.2.2: Hospital monitoring system -- 3.3: Experimentation -- 3.3.1: The threshold for the Levenshtein distance -- 3.3.2: The threshold for heart rate and respiratory rate | |
| 505 | 8 | |a 3.4: Conclusion -- References -- Chapter 4: Application of Machine Learning in Chest X-ray Images -- 4.1: Introduction -- 4.2: Chest X-ray Images -- 4.3: Literature Review -- 4.4: Application of Machine Learning in Chest X-ray Images -- 4.4.1: Clustering -- 4.4.2: Regression -- 4.4.3: Segmentation -- 4.4.4: Classification -- 4.5: Case Study: Lung Chest X-ray Images -- 4.5.1: Methodology -- 4.5.2: JSRT dataset -- 4.5.3: Image pre-processing -- 4.5.4: CNN -- 4.6: Conclusion -- 4.7: Future Study -- References -- Chapter 5: Integrated Solution for Chest X-ray Image Classification -- 5.1: Introduction | |
| 505 | 8 | |a 5.2: Related Work -- 5.3: The Method -- 5.3.1: Feature extraction -- 5.3.2: Feature reduction -- 5.3.3: Classification -- 5.3.4: Algorithm -- 5.4: Experimental Results -- 5.5: Discussion and Conclusions -- References -- Chapter 6: Predicting Genetic Mutations Among Cancer Patients by Incorporating LSTM with Word Embedding Techniques -- 6.1: Introduction -- 6.2: Related Work -- 6.2.1: Basic feature engineering -- 6.2.2: Classification method -- 6.3: Data Overview -- 6.3.1: Dataset structure -- 6.3.2: Data pre-processing -- 6.3.3: Most frequent genes and class -- 6.3.4: Most frequent variation and class | |
| 520 | 3 | |a This book provides various insights into machine learning techniques in healthcare system data and its analysis | |
| 653 | 0 | |a Machine learning | |
| 653 | 0 | |a Artificial intelligence / Medical applications | |
| 653 | 0 | |a Medical informatics | |
| 653 | 0 | |a Machine Learning | |
| 653 | 0 | |a Apprentissage automatique | |
| 653 | 0 | |a Intelligence artificielle en médecine | |
| 653 | 0 | |a Médecine / Informatique | |
| 653 | 0 | |a COMPUTERS / Information Technology | |
| 653 | 0 | |a Artificial intelligence / Medical applications | |
| 653 | 0 | |a Machine learning | |
| 653 | 0 | |a Medical informatics | |
| 700 | 1 | |a Chandran, C. Karthik |e Sonstige |4 oth | |
| 700 | 1 | |a Rajalakshmi, M. |e Sonstige |4 oth | |
| 700 | 1 | |a Mohanty, Sachi Nandan |d 1981- |e Sonstige |0 (DE-588)116301849X |4 oth | |
| 700 | 1 | |a Chowdhury, Subrata |e Sonstige |4 oth | |
| 776 | 0 | 8 | |i Print version |a Chandran, C. Karthik |t Machine Learning for Healthcare Systems |d Gistrup, Denmark: River Publishers,c2023 |z 9788770228114 |
| 856 | 4 | 0 | |u https://ieeexplore.ieee.org/book/10089364 |x Aggregator |z URL des Erstveröffentlichers |3 Volltext |
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Datensatz im Suchindex
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| contents | Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- List of Contributors -- List of Figures -- List of Tables -- List of Abbreviations -- Chapter 1: Investigation on Improving the Performance of Class-imbalanced Medical Health Datasets -- 1.1: Introduction -- 1.2: Problem Formulation -- 1.3: Techniques to Handle Imbalanced Datasets -- 1.3.1: Random undersampling (RUS) -- 1.3.2: Random oversampling (RUS) -- 1.3.3: Synthetic minority oversampling technique (SMOTE) -- 1.4: Classification Models -- 1.4.1: Naive Bayes 1.4.2: k-Nearest neighbor classifier -- 1.4.3: Decision tree classifier -- 1.4.4: Random forest -- 1.5: Dataset Collection -- 1.5.1: Heart failure clinical records dataset -- 1.5.2: Diabetes dataset -- 1.6: Experimental Results and Discussion -- 1.6.1: Heart failure clinical records dataset -- 1.6.2: Diabetes dataset -- 1.7: Conclusion -- References -- Chapter 2: Improving Heart Disease Diagnosis using Modified Dynamic Adaptive PSO (MDAPSO) -- 2.1: Introduction -- 2.2: Background and Related Work -- 2.2.1: PSO modification -- 2.2.2: Feature selection -- 2.2.3: Classification and clustering 2.3: Proposed Approach -- 2.3.1: PSO algorithm -- 2.3.2: Inertia weight -- 2.3.3: Modified dynamic adaptive particle swarm optimization (MDAPSO) -- 2.4: Experimental Setup and Results -- 2.4.1: Dataset -- 2.4.2: Performance metrics -- 2.4.3: Result -- 2.5: Conclusion -- References -- Chapter 3: Efficient Diagnosis and ICU Patient Monitoring Model -- 3.1: Introduction -- 3.2: Main Text -- 3.2.1: Disease prediction -- 3.2.2: Hospital monitoring system -- 3.3: Experimentation -- 3.3.1: The threshold for the Levenshtein distance -- 3.3.2: The threshold for heart rate and respiratory rate 3.4: Conclusion -- References -- Chapter 4: Application of Machine Learning in Chest X-ray Images -- 4.1: Introduction -- 4.2: Chest X-ray Images -- 4.3: Literature Review -- 4.4: Application of Machine Learning in Chest X-ray Images -- 4.4.1: Clustering -- 4.4.2: Regression -- 4.4.3: Segmentation -- 4.4.4: Classification -- 4.5: Case Study: Lung Chest X-ray Images -- 4.5.1: Methodology -- 4.5.2: JSRT dataset -- 4.5.3: Image pre-processing -- 4.5.4: CNN -- 4.6: Conclusion -- 4.7: Future Study -- References -- Chapter 5: Integrated Solution for Chest X-ray Image Classification -- 5.1: Introduction 5.2: Related Work -- 5.3: The Method -- 5.3.1: Feature extraction -- 5.3.2: Feature reduction -- 5.3.3: Classification -- 5.3.4: Algorithm -- 5.4: Experimental Results -- 5.5: Discussion and Conclusions -- References -- Chapter 6: Predicting Genetic Mutations Among Cancer Patients by Incorporating LSTM with Word Embedding Techniques -- 6.1: Introduction -- 6.2: Related Work -- 6.2.1: Basic feature engineering -- 6.2.2: Classification method -- 6.3: Data Overview -- 6.3.1: Dataset structure -- 6.3.2: Data pre-processing -- 6.3.3: Most frequent genes and class -- 6.3.4: Most frequent variation and class |
| ctrlnum | (OCoLC)1418694093 (DE-599)BVBBV049498097 |
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| id | DE-604.BV049498097 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T23:20:49Z |
| indexdate | 2024-07-10T10:08:58Z |
| institution | BVB |
| isbn | 9781000959994 1000959996 8770228116 9788770228114 9781000959987 1000959988 9788770228107 8770228108 9781003438816 1003438814 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034843285 |
| oclc_num | 1418694093 |
| open_access_boolean | |
| owner | DE-573 |
| owner_facet | DE-573 |
| physical | 1 Online-Ressource (xxvii, 222 Seiten) Diagramme |
| psigel | ZDB-37-RPEB |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | River Publishers |
| record_format | marc |
| series2 | River Publishers Series in Computing and Information Science and Technology |
| spelling | Machine learning for healthcare systems foundations and applications Gistrup, Denmark River Publishers [2023] 1 Online-Ressource (xxvii, 222 Seiten) Diagramme txt rdacontent c rdamedia cr rdacarrier River Publishers Series in Computing and Information Science and Technology Description based upon print version of record Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- List of Contributors -- List of Figures -- List of Tables -- List of Abbreviations -- Chapter 1: Investigation on Improving the Performance of Class-imbalanced Medical Health Datasets -- 1.1: Introduction -- 1.2: Problem Formulation -- 1.3: Techniques to Handle Imbalanced Datasets -- 1.3.1: Random undersampling (RUS) -- 1.3.2: Random oversampling (RUS) -- 1.3.3: Synthetic minority oversampling technique (SMOTE) -- 1.4: Classification Models -- 1.4.1: Naive Bayes 1.4.2: k-Nearest neighbor classifier -- 1.4.3: Decision tree classifier -- 1.4.4: Random forest -- 1.5: Dataset Collection -- 1.5.1: Heart failure clinical records dataset -- 1.5.2: Diabetes dataset -- 1.6: Experimental Results and Discussion -- 1.6.1: Heart failure clinical records dataset -- 1.6.2: Diabetes dataset -- 1.7: Conclusion -- References -- Chapter 2: Improving Heart Disease Diagnosis using Modified Dynamic Adaptive PSO (MDAPSO) -- 2.1: Introduction -- 2.2: Background and Related Work -- 2.2.1: PSO modification -- 2.2.2: Feature selection -- 2.2.3: Classification and clustering 2.3: Proposed Approach -- 2.3.1: PSO algorithm -- 2.3.2: Inertia weight -- 2.3.3: Modified dynamic adaptive particle swarm optimization (MDAPSO) -- 2.4: Experimental Setup and Results -- 2.4.1: Dataset -- 2.4.2: Performance metrics -- 2.4.3: Result -- 2.5: Conclusion -- References -- Chapter 3: Efficient Diagnosis and ICU Patient Monitoring Model -- 3.1: Introduction -- 3.2: Main Text -- 3.2.1: Disease prediction -- 3.2.2: Hospital monitoring system -- 3.3: Experimentation -- 3.3.1: The threshold for the Levenshtein distance -- 3.3.2: The threshold for heart rate and respiratory rate 3.4: Conclusion -- References -- Chapter 4: Application of Machine Learning in Chest X-ray Images -- 4.1: Introduction -- 4.2: Chest X-ray Images -- 4.3: Literature Review -- 4.4: Application of Machine Learning in Chest X-ray Images -- 4.4.1: Clustering -- 4.4.2: Regression -- 4.4.3: Segmentation -- 4.4.4: Classification -- 4.5: Case Study: Lung Chest X-ray Images -- 4.5.1: Methodology -- 4.5.2: JSRT dataset -- 4.5.3: Image pre-processing -- 4.5.4: CNN -- 4.6: Conclusion -- 4.7: Future Study -- References -- Chapter 5: Integrated Solution for Chest X-ray Image Classification -- 5.1: Introduction 5.2: Related Work -- 5.3: The Method -- 5.3.1: Feature extraction -- 5.3.2: Feature reduction -- 5.3.3: Classification -- 5.3.4: Algorithm -- 5.4: Experimental Results -- 5.5: Discussion and Conclusions -- References -- Chapter 6: Predicting Genetic Mutations Among Cancer Patients by Incorporating LSTM with Word Embedding Techniques -- 6.1: Introduction -- 6.2: Related Work -- 6.2.1: Basic feature engineering -- 6.2.2: Classification method -- 6.3: Data Overview -- 6.3.1: Dataset structure -- 6.3.2: Data pre-processing -- 6.3.3: Most frequent genes and class -- 6.3.4: Most frequent variation and class This book provides various insights into machine learning techniques in healthcare system data and its analysis Machine learning Artificial intelligence / Medical applications Medical informatics Machine Learning Apprentissage automatique Intelligence artificielle en médecine Médecine / Informatique COMPUTERS / Information Technology Chandran, C. Karthik Sonstige oth Rajalakshmi, M. Sonstige oth Mohanty, Sachi Nandan 1981- Sonstige (DE-588)116301849X oth Chowdhury, Subrata Sonstige oth Print version Chandran, C. Karthik Machine Learning for Healthcare Systems Gistrup, Denmark: River Publishers,c2023 9788770228114 https://ieeexplore.ieee.org/book/10089364 Aggregator URL des Erstveröffentlichers Volltext https://public.ebookcentral.proquest.com/choice/PublicFullRecord.aspx?p=7275994 https://www.taylorfrancis.com/books/9781003438816 Taylor & Francis |
| spellingShingle | Machine learning for healthcare systems foundations and applications Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- List of Contributors -- List of Figures -- List of Tables -- List of Abbreviations -- Chapter 1: Investigation on Improving the Performance of Class-imbalanced Medical Health Datasets -- 1.1: Introduction -- 1.2: Problem Formulation -- 1.3: Techniques to Handle Imbalanced Datasets -- 1.3.1: Random undersampling (RUS) -- 1.3.2: Random oversampling (RUS) -- 1.3.3: Synthetic minority oversampling technique (SMOTE) -- 1.4: Classification Models -- 1.4.1: Naive Bayes 1.4.2: k-Nearest neighbor classifier -- 1.4.3: Decision tree classifier -- 1.4.4: Random forest -- 1.5: Dataset Collection -- 1.5.1: Heart failure clinical records dataset -- 1.5.2: Diabetes dataset -- 1.6: Experimental Results and Discussion -- 1.6.1: Heart failure clinical records dataset -- 1.6.2: Diabetes dataset -- 1.7: Conclusion -- References -- Chapter 2: Improving Heart Disease Diagnosis using Modified Dynamic Adaptive PSO (MDAPSO) -- 2.1: Introduction -- 2.2: Background and Related Work -- 2.2.1: PSO modification -- 2.2.2: Feature selection -- 2.2.3: Classification and clustering 2.3: Proposed Approach -- 2.3.1: PSO algorithm -- 2.3.2: Inertia weight -- 2.3.3: Modified dynamic adaptive particle swarm optimization (MDAPSO) -- 2.4: Experimental Setup and Results -- 2.4.1: Dataset -- 2.4.2: Performance metrics -- 2.4.3: Result -- 2.5: Conclusion -- References -- Chapter 3: Efficient Diagnosis and ICU Patient Monitoring Model -- 3.1: Introduction -- 3.2: Main Text -- 3.2.1: Disease prediction -- 3.2.2: Hospital monitoring system -- 3.3: Experimentation -- 3.3.1: The threshold for the Levenshtein distance -- 3.3.2: The threshold for heart rate and respiratory rate 3.4: Conclusion -- References -- Chapter 4: Application of Machine Learning in Chest X-ray Images -- 4.1: Introduction -- 4.2: Chest X-ray Images -- 4.3: Literature Review -- 4.4: Application of Machine Learning in Chest X-ray Images -- 4.4.1: Clustering -- 4.4.2: Regression -- 4.4.3: Segmentation -- 4.4.4: Classification -- 4.5: Case Study: Lung Chest X-ray Images -- 4.5.1: Methodology -- 4.5.2: JSRT dataset -- 4.5.3: Image pre-processing -- 4.5.4: CNN -- 4.6: Conclusion -- 4.7: Future Study -- References -- Chapter 5: Integrated Solution for Chest X-ray Image Classification -- 5.1: Introduction 5.2: Related Work -- 5.3: The Method -- 5.3.1: Feature extraction -- 5.3.2: Feature reduction -- 5.3.3: Classification -- 5.3.4: Algorithm -- 5.4: Experimental Results -- 5.5: Discussion and Conclusions -- References -- Chapter 6: Predicting Genetic Mutations Among Cancer Patients by Incorporating LSTM with Word Embedding Techniques -- 6.1: Introduction -- 6.2: Related Work -- 6.2.1: Basic feature engineering -- 6.2.2: Classification method -- 6.3: Data Overview -- 6.3.1: Dataset structure -- 6.3.2: Data pre-processing -- 6.3.3: Most frequent genes and class -- 6.3.4: Most frequent variation and class |
| title | Machine learning for healthcare systems foundations and applications |
| title_auth | Machine learning for healthcare systems foundations and applications |
| title_exact_search | Machine learning for healthcare systems foundations and applications |
| title_exact_search_txtP | Machine learning for healthcare systems foundations and applications |
| title_full | Machine learning for healthcare systems foundations and applications |
| title_fullStr | Machine learning for healthcare systems foundations and applications |
| title_full_unstemmed | Machine learning for healthcare systems foundations and applications |
| title_short | Machine learning for healthcare systems |
| title_sort | machine learning for healthcare systems foundations and applications |
| title_sub | foundations and applications |
| url | https://ieeexplore.ieee.org/book/10089364 https://public.ebookcentral.proquest.com/choice/PublicFullRecord.aspx?p=7275994 https://www.taylorfrancis.com/books/9781003438816 |
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