Internet of Things and Personalized Healthcare Systems

139 Pages·2019·5.22 MB·English

by  P. Venkata Krishna, Sasikumar Gurumoorthy, Mohammad S. Obaidat

S P R I N G E R B R I E F S I N A P P L I E D S C I E N C E S A N D
T E C H N O LO G Y  FORENSIC AND MEDICAL BIOINFORMATICS
P. Venkata Krishna 
Sasikumar Gurumoorthy 
Mohammad S. Obaidat
Internet of Things
and Personalized
Healthcare
Systems
SpringerBriefs in Applied Sciences
and Technology
Forensic and Medical Bioinformatics
Series editors
Amit Kumar, Hyderabad, India
Allam Appa Rao, Hyderabad, India
More information about this series at http://www.springer.com/series/11910
P. Venkata Krishna • Sasikumar Gurumoorthy
Mohammad S. Obaidat
Internet of Things
and Personalized Healthcare
Systems
123
P. Venkata Krishna Mohammad S. Obaidat
Department of Computer Science Department of Computer
Sri Padmavati Mahila Visvavidyalayam and Information Science
Tirupati, Andhra Pradesh, India Fordham University
Bronx, NY, USA
Sasikumar Gurumoorthy
Department of Computer Science
and Systems Engineering
Sree Vidyanikethan Engineering College
Tirupati, Andhra Pradesh, India
ISSN 2191-530X ISSN 2191-5318 (electronic)
SpringerBriefs in Applied Sciences and Technology
ISSN 2196-8845 ISSN 2196-8853 (electronic)
SpringerBriefs in Forensic and Medical Bioinformatics
ISBN 978-981-13-0865-9 ISBN 978-981-13-0866-6 (eBook)
https://doi.org/10.1007/978-981-13-0866-6
Library of Congress Control Number: 2018957056
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2019
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Contents
1 Sensitivity Analysis of Micro Mass Optical MEMS Sensor
for Biomedical IoT Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Mala Serene, Rajasekhara Babu and Zachariah C. Alex
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Modeling and Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Different Shapes of Cantilever . . . . . . . . . . . . . . . . . . . . . . . . 3
1.4 Rectangular-Shaped Micro Mass Optical MEMS Sensor . . . . . 4
1.5 Trapezoidal/Triangular-Shaped Micro Mass Optical MEMS
Sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.6 Step Profile-Shaped Micro Mass Optical MEMS Sensor . . . . . 7
1.7 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2 Enhancing the Performance of Decision Tree Using NSUM
Technique for Diabetes Patients . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Nithya Settu and M. Rajasekhara Babu
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.3 Mutual Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.3.1 Symmetric Uncertainty . . . . . . . . . . . . . . . . . . . . . . . 17
2.3.2 Proposed Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.4 Experimental Result and Discussion . . . . . . . . . . . . . . . . . . . . 18
2.5 Conclusion and Future Scope . . . . . . . . . . . . . . . . . . . . . . . . . 19
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
v
vi Contents
3 A Novel Framework for Healthcare Monitoring System
Through Cyber-Physical System . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
K. Monisha and M. Rajasekhara Babu
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.2.1 Wireless Body Area Network (WBAN)
in Healthcare System . . . . . . . . . . . . . . . . . . . . . . . . 23
3.2.2 Electronic Health Record (EHR) Assisted
by Cloud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.2.3 Data Security in Healthcare Application . . . . . . . . . . . 26
3.3 Framework for Healthcare Application Through CPS . . . . . . . 27
3.4 Internet of Medical Things (IoMT) . . . . . . . . . . . . . . . . . . . . . 29
3.5 Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.6 Result and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4 An IoT Model to Improve Cognitive Skills of Student
Learning Experience Using Neurosensors . . . . . . . . . . . . . . . . . . . . 37
Abhishek Padhi, M. Rajasekhara Babu, Bhasker Jha
and Shrutisha Joshi
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.1.1 Needs or Requirements . . . . . . . . . . . . . . . . . . . . . . . 37
4.1.2 Why This Work? . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.1.3 ThinkGear Measurements (MindSet Pro/TGEM) . . . . . 40
4.2 Existing Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.3 Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.4 Result and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
5 AdaBoost with Feature Selection Using IoT to Bring
the Paths for Somatic Mutations Evaluation in Cancer . . . . . . . . . 51
Anuradha Chokka and K. Sandhya Rani
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
5.1.1 AdaBoost Technique . . . . . . . . . . . . . . . . . . . . . . . . . 52
5.1.2 Feature Selection Techniques . . . . . . . . . . . . . . . . . . 52
5.1.3 Internet of Things (IoT) . . . . . . . . . . . . . . . . . . . . . . 53
5.1.4 Challenges in Sequencing . . . . . . . . . . . . . . . . . . . . . 53
5.2 Existing Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
5.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
5.3.1 Redundancy and Relevancy Analysis Approach . . . . . 55
5.3.2 Feature Redundancy and Feature Relevancy . . . . . . . . 56
5.3.3 Defining a Framework of AdaBoost Technique
with Feature Selection . . . . . . . . . . . . . . . . . . . . . . . . 56
Contents vii
5.3.4 Schematic Representation for the Proposed
Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
5.3.5 Algorithm and Analysis . . . . . . . . . . . . . . . . . . . . . . 57
5.3.6 IoT Wearables to Detect Cancer . . . . . . . . . . . . . . . . 58
5.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
6 A Fuzzy-Based Expert System to Diagnose Alzheimer’s
Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
R. M. Mallika, K. UshaRani and K. Hemalatha
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
6.2 Literature Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
6.3 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
6.3.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
6.3.2 Proposed Methodology . . . . . . . . . . . . . . . . . . . . . . . 67
6.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
6.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
7 Secured Architecture for Internet of Things-Enabled
Personalized Healthcare Systems . . . . . . . . . . . . . . . . . . . . . . . . . . 75
Vikram Neerugatti and A. Rama Mohan Reddy
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
7.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
7.3 Proposed Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
7.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
8 Role of Imaging Modality in Premature Detection of Bosom
Irregularity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
Modepalli Kavitha, P. Venkata Krishna and V. Saritha
8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
8.2 Mammography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
8.3 Thermography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
8.4 Result Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
8.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
8.6 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
9 Healthcare Application Development in Mobile
and Cloud Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
B. Mallikarjuna and D. Arun Kumar Reddy
9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
9.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
9.3 Analysis of Health Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . 95
viii Contents
9.4 Proposed Application Overview . . . . . . . . . . . . . . . . . . . . . . . 97
9.5 Experimental Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
9.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
10 A Computational Approach to Predict Diabetic Retinopathy
Through Data Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
Ashraf Ali Shaik, Ch Prathima and Naresh Babu Muppalaneni
10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
10.1.1 Steps in Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 107
10.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
10.2.1 Description of Dataset . . . . . . . . . . . . . . . . . . . . . . . . 107
10.2.2 Attribute Information . . . . . . . . . . . . . . . . . . . . . . . . 108
10.2.3 Cross-Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
10.2.4 Classification Matrix . . . . . . . . . . . . . . . . . . . . . . . . . 108
10.2.5 Bagging and Boosting . . . . . . . . . . . . . . . . . . . . . . . . 109
10.3 Performance Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
10.3.1 Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
10.3.2 Sensitivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
10.3.3 Specificity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
10.3.4 Classification Matrix . . . . . . . . . . . . . . . . . . . . . . . . . 110
10.4 Tools Used and Results Discussion . . . . . . . . . . . . . . . . . . . . 110
10.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
11 Diagnosis of Chest Diseases Using Artificial Neural Networks . . . . 113
Himaja Gadi, G. Lavanya Devi and N. Ramesh
11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
11.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
11.3 Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
11.4 Types of Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
11.5 Back-Propagation Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 116
11.6 Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
11.7 Validation Checks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
11.8 Results and Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
11.9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
12 Study on Efficient and Adaptive Reproducing Management in
Hadoop Distributed File System . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
P. Satheesh, B. Srinivas, P. R. S. Naidu and B. Prasanth Kumar
12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
12.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
Contents ix
12.2.1 Distributed Storage . . . . . . . . . . . . . . . . . . . . . . . . . . 123
12.2.2 Information Replication . . . . . . . . . . . . . . . . . . . . . . . 124
12.2.3 Replica Placement . . . . . . . . . . . . . . . . . . . . . . . . . . 125
12.3 Existing System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
12.3.1 Data Locality Problem . . . . . . . . . . . . . . . . . . . . . . . 127
12.4 Proposed System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
12.4.1 System Description . . . . . . . . . . . . . . . . . . . . . . . . . . 127
12.4.2 Replication Management . . . . . . . . . . . . . . . . . . . . . . 129
12.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
12.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131