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Handbook of research on disease pred...

Rani, Geeta, (1980-)

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Handbook of research on disease prediction through data analytics and machine learning

Record Type:	Electronic resources : Monograph/item
Title/Author:	Handbook of research on disease prediction through data analytics and machine learning/ Geeta Rani and Pradeep Kumar Tiwari, editors.
other author:	Rani, Geeta,
Published:	Hershey, Pennsylvania :IGI Global, : 2020.,
Description:	1 online resource (xxx, 586 p.)
[NT 15003449]:	Chapter 1. Glaucoma detection using convolutional neural networks -- Chapter 2. Pre-processing highly sparse and frequently evolving standardized electronic health records for mining -- Chapter 3. Image classification techniques -- Chapter 4. Prediction models -- Chapter 5. Prediction models for healthcare using machine learning: a review -- Chapter 6. Chronic kidney disease prediction using data mining algorithms -- Chapter 7. A machine learning approach to prevent cancer -- Chapter 8. Machine learning perspective in cancer research -- Chapter 9. A pathway to differential modelling of Nipah virus -- Chapter 10. Application of AI for computer-aided diagnosis system to detect brain tumors -- Chapter 11. Application of machine learning to analyse biomedical signals for medical diagnosis -- Chapter 12. Artificial bee colony-based associative classifier for healthcare data diagnosis -- Chapter 13. Artificial intelligence approaches to detect neurodegenerative disease from medical records: a perspective -- Chapter 14. Clinical decision support systems: decision-making system for clinical data -- Chapter 15. Diagnosis and prognosis of ultrasound fetal growth analysis using neuro-fuzzy based on genetic algorithms -- Chapter 16. ECG image classification using deep learning approach -- Chapter 17. Genetic data analysis -- Chapter 18. Heart disease prediction using machine learning -- Chapter 19. Heuristic approach performances for artificial neural networks training -- Chapter 20. Mental health through biofeedback is important to analyze: an app and analysis -- Chapter 21. Pre-clustering techniques for healthcare system: evaluation measures, evaluation metrics, comparative study of existing vs. proposed approaches -- Chapter 22. Strategic analysis in prediction of liver disease using different classification algorithms -- Chapter 23. Texture segmentation and features of medical images -- Chapter 24. Towards integrating data mining with knowledge-based system for diagnosis of human eye diseases: the case of an African hospital -- Chapter 25. Use of IoT and different biofeedback to measure TTH: an approach for healthcare 4.0 -- Chapter 26. ACO_NB-based hybrid prediction model for medical disease diagnosis.
Subject:	Machine learning. -
Online resource:	http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-7998-2742-9
ISBN:	9781799827436 (ebk.)

Handbook of research on disease prediction through data analytics and machine learning
Handbook of research on disease prediction through data analytics and machine learning[electronic resource] /Geeta Rani and Pradeep Kumar Tiwari, editors. - Hershey, Pennsylvania :IGI Global,2020. - 1 online resource (xxx, 586 p.)

Includes bibliographical references and index.

Chapter 1. Glaucoma detection using convolutional neural networks -- Chapter 2. Pre-processing highly sparse and frequently evolving standardized electronic health records for mining -- Chapter 3. Image classification techniques -- Chapter 4. Prediction models -- Chapter 5. Prediction models for healthcare using machine learning: a review -- Chapter 6. Chronic kidney disease prediction using data mining algorithms -- Chapter 7. A machine learning approach to prevent cancer -- Chapter 8. Machine learning perspective in cancer research -- Chapter 9. A pathway to differential modelling of Nipah virus -- Chapter 10. Application of AI for computer-aided diagnosis system to detect brain tumors -- Chapter 11. Application of machine learning to analyse biomedical signals for medical diagnosis -- Chapter 12. Artificial bee colony-based associative classifier for healthcare data diagnosis -- Chapter 13. Artificial intelligence approaches to detect neurodegenerative disease from medical records: a perspective -- Chapter 14. Clinical decision support systems: decision-making system for clinical data -- Chapter 15. Diagnosis and prognosis of ultrasound fetal growth analysis using neuro-fuzzy based on genetic algorithms -- Chapter 16. ECG image classification using deep learning approach -- Chapter 17. Genetic data analysis -- Chapter 18. Heart disease prediction using machine learning -- Chapter 19. Heuristic approach performances for artificial neural networks training -- Chapter 20. Mental health through biofeedback is important to analyze: an app and analysis -- Chapter 21. Pre-clustering techniques for healthcare system: evaluation measures, evaluation metrics, comparative study of existing vs. proposed approaches -- Chapter 22. Strategic analysis in prediction of liver disease using different classification algorithms -- Chapter 23. Texture segmentation and features of medical images -- Chapter 24. Towards integrating data mining with knowledge-based system for diagnosis of human eye diseases: the case of an African hospital -- Chapter 25. Use of IoT and different biofeedback to measure TTH: an approach for healthcare 4.0 -- Chapter 26. ACO_NB-based hybrid prediction model for medical disease diagnosis.

ISBN: 9781799827436 (ebk.)Subjects--Topical Terms:

533906
Machine learning.

LC Class. No.: R853.S7 / H36 2020

Dewey Class. No.: 616.0072/7

Handbook of research on disease prediction through data analytics and machine learning
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020 $a 9781799827436 (ebk.)
020 $a 9781799827429 (hbk.)
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245 0 0 $a Handbook of research on disease prediction through data analytics and machine learning $h [electronic resource] / $c Geeta Rani and Pradeep Kumar Tiwari, editors.
260 $a Hershey, Pennsylvania : $b IGI Global, $c 2020.
300 $a 1 online resource (xxx, 586 p.)
504 $a Includes bibliographical references and index.
505 0 $a Chapter 1. Glaucoma detection using convolutional neural networks -- Chapter 2. Pre-processing highly sparse and frequently evolving standardized electronic health records for mining -- Chapter 3. Image classification techniques -- Chapter 4. Prediction models -- Chapter 5. Prediction models for healthcare using machine learning: a review -- Chapter 6. Chronic kidney disease prediction using data mining algorithms -- Chapter 7. A machine learning approach to prevent cancer -- Chapter 8. Machine learning perspective in cancer research -- Chapter 9. A pathway to differential modelling of Nipah virus -- Chapter 10. Application of AI for computer-aided diagnosis system to detect brain tumors -- Chapter 11. Application of machine learning to analyse biomedical signals for medical diagnosis -- Chapter 12. Artificial bee colony-based associative classifier for healthcare data diagnosis -- Chapter 13. Artificial intelligence approaches to detect neurodegenerative disease from medical records: a perspective -- Chapter 14. Clinical decision support systems: decision-making system for clinical data -- Chapter 15. Diagnosis and prognosis of ultrasound fetal growth analysis using neuro-fuzzy based on genetic algorithms -- Chapter 16. ECG image classification using deep learning approach -- Chapter 17. Genetic data analysis -- Chapter 18. Heart disease prediction using machine learning -- Chapter 19. Heuristic approach performances for artificial neural networks training -- Chapter 20. Mental health through biofeedback is important to analyze: an app and analysis -- Chapter 21. Pre-clustering techniques for healthcare system: evaluation measures, evaluation metrics, comparative study of existing vs. proposed approaches -- Chapter 22. Strategic analysis in prediction of liver disease using different classification algorithms -- Chapter 23. Texture segmentation and features of medical images -- Chapter 24. Towards integrating data mining with knowledge-based system for diagnosis of human eye diseases: the case of an African hospital -- Chapter 25. Use of IoT and different biofeedback to measure TTH: an approach for healthcare 4.0 -- Chapter 26. ACO_NB-based hybrid prediction model for medical disease diagnosis.
650 0 $a Machine learning. $3 533906
650 0 $a Diagnostic imaging $x Digital techniques. $3 813376
650 0 $a Image analysis. $3 561018
650 0 $a Fuzzy logic. $3 532071
650 0 $a Sampling (Statistics) $3 545623
700 1 $a Rani, Geeta, $d 1980- $3 3510605
700 1 $a Tiwari, Pradeep Kumar, $d 1982- $3 3510606
856 4 0 $u http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-7998-2742-9