Anmerkung: Front Cover -- Machine Learning and the Internet of Medical Things in Healthcare -- Copyright Page -- Contents -- List of contributors -- 1 Machine learning architecture and framework -- 1.1 Introduction -- 1.1.1 Machine learning classification -- 1.1.1.1 Supervised learning -- 1.1.1.2 Unsupervised learning -- 1.1.1.3 Reinforcement learning -- 1.2 Architecture of machine learning -- 1.2.1 Data acquisition -- 1.2.2 Data processing -- 1.2.2.1 Arrangement of data -- 1.2.2.2 Analysis of data -- 1.2.2.3 Preprocessing of data -- 1.2.2.4 Transformation of data -- 1.2.3 Data modeling -- 1.2.4 Execution (model evaluation) -- 1.2.5 Deployment -- 1.3 Machine learning framework -- 1.3.1 Features of ML framework -- 1.3.2 Types of ML framework -- 1.3.2.1 TensorFlow -- 1.3.2.2 Amazon machine learning -- 1.3.2.3 Scikit-learn -- 1.3.2.4 Apache mahout -- 1.3.2.5 Cognitive toolkit of microsoft -- 1.4 Significance of machine learning in the healthcare system -- 1.4.1 Machine-learning applications in the healthcare system -- 1.4.1.1 Identification and diagnosis of disease -- 1.4.1.2 Discovery and manufacturing of drugs -- 1.4.1.3 Diagnosis through medical imaging -- 1.5 Conclusion -- References -- 2 Machine learning in healthcare: review, opportunities and challenges -- 2.1 Introduction -- 2.1.1 Machine learning in a nutshell -- 2.1.2 Machine learning techniques and applications -- 2.1.2.1 Supervised learning -- 2.1.2.2 Unsupervised learning -- 2.1.2.3 Reinforcement learning -- 2.1.3 Desired features of machine learning -- 2.1.4 How machine learning works? -- 2.1.5 Why machine learning for healthcare? -- 2.2 Analysis of domain -- 2.2.1 Background and related works -- 2.2.2 Integration scenarios of ML and Healthcare -- 2.2.3 Existing machine learning applications for healthcare -- 2.3 Perspective of disease diagnosis using machine learning. , 2.3.1 Future perspective to enhance healthcare system using machine learning -- 2.3.1.1 Challenges and risks -- 2.4 Conclusions -- References -- 3 Machine learning for biomedical signal processing -- 3.1 Introduction -- 3.2 Reviews of ECG signal -- 3.3 Preprocessing of ECG signal using ML based techniques -- 3.3.1 Least mean square (LMS) -- 3.3.2 Normalized least mean square (NLMS) -- 3.3.3 Delayed error normalized LMS (DENLMS) algorithm -- 3.3.4 Sign data least mean square (SDLMS) -- 3.3.5 Log least mean square (LLMS) -- 3.4 Feature extraction and classification of ECG signal using ML-based techniques -- 3.4.1 Artificial neural network (ANN) -- 3.4.2 Fuzzy logic (FL) -- 3.4.3 Wavelet transforms -- 3.4.4 Hybrid approach -- 3.5 Discussions and conclusions -- References -- 4 Artificial itelligence in medicine -- 4.1 Introduction -- 4.1.1 Disease -- 4.1.1.1 Autoimmune diseases -- 4.1.1.2 Classification of diseases -- 4.1.1.3 Concept of diagnosis and treatment -- 4.1.2 Medicine -- 4.1.2.1 Medicine working -- 4.1.2.2 Different types of medicines -- 4.1.2.3 Discovering new medicines -- 4.1.2.4 Role of intelligent algorithm -- 4.1.2.5 History of AI -- 4.1.3 History of AI in medicine -- 4.1.4 Drug discovery process -- 4.1.5 Machine-learning algorithms in medicine -- 4.1.5.1 Linear regression -- 4.1.5.2 Logistic regression -- 4.1.5.3 Support vector machine -- 4.1.5.4 Convolutional neural network -- 4.1.6 Expert systems -- 4.1.7 Fuzzy expert systems -- 4.1.8 Artificial neural networks -- 4.2 Conclusion -- References -- 5 Diagnosing of disease using machine learning -- 5.1 Introduction -- 5.2 Background and related work -- 5.2.1 Challenges in conventional healthcare system -- 5.2.2 Machine-learning tools for diagnosis and prediction -- 5.2.3 Python -- 5.2.4 MATLAB -- 5.3 Types of machine-learning algorithm -- 5.4 Diagnosis model for disease prediction. , 5.4.1 Data preprocessing -- 5.4.2 Training and testing data set -- 5.4.3 Classification technique -- 5.4.4 Performance metrics -- 5.5 Confusion matrix -- 5.6 Disease diagnosis by various machine-learning algorithms -- 5.6.1 Support vector machine (SVM) -- 5.6.2 K-nearest neighbors (KNN) -- 5.6.3 Decision tree (DT) -- 5.6.4 Naive bayes (NB) -- 5.7 ML algorithm in neurological, cardiovascular, and cancer disease diagnosis -- 5.7.1 Neurological disease diagnosis by machine learning -- 5.7.2 Cardiovascular disease diagnosis by machine learning -- 5.7.3 Breast cancer diagnosis and prediction: a case study -- 5.7.3.1 Performance evaluation of breast cancer data set -- 5.7.4 Impact of machine learning in the healthcare industry -- 5.8 Conclusion and future scope -- References -- 6 A novel approach of telemedicine for managing fetal condition based on machine learning technology from IoT-based wearabl... -- 6.1 Introduction -- 6.2 Healthcare and big data -- 6.3 Big data analytics -- 6.4 Need of IOT in the healthcare industry -- 6.5 Healthcare uses machine learning -- 6.6 Need for machine learning -- 6.7 Cardiotocography -- 6.8 Literature review -- 6.8.1 Research on revolutionary effect of telemedicine and its history -- 6.8.2 Role of machine learning in telemedicine/healthcare -- 6.8.3 Role of big data analytics in healthcare -- 6.8.4 Challenges faced in handling big data in healthcare/telemedicine -- 6.8.5 Research done on tracing the fetal well-being using telemedicine and machine learning algorithms -- 6.9 Methodology -- 6.9.1 Preprocessing and splitting of data -- 6.10 Evaluation -- 6.11 Conclusion and future work -- References -- 7 IoT-based healthcare delivery services to promote transparency and patient satisfaction in a corporate hospital -- 7.1 Introduction -- 7.2 Uses of IoT in healthcare -- 7.3 Main problem area of a corporate hospital. , 7.3.1 Location -- 7.3.2 Hassle on outpatient services -- 7.3.3 Diagnostic services -- 7.3.4 Inpatient services -- 7.3.5 Support and utility services -- 7.3.6 Coordination in medical section -- 7.3.7 Medical record keeping -- 7.3.8 Transparency -- 7.3.9 Cost leadership model in market -- 7.4 Implementation of IoT-based healthcare delivery services -- 7.4.1 The work of value chain -- 7.4.1.1 IoT and medical record -- 7.4.1.2 IoT and therapeutic facilities -- 7.4.1.3 IoT in supportive and utility services -- 7.4.1.4 IoT in patient delight -- 7.4.1.5 Cost leadership with quality of care -- 7.5 Conclusion -- References -- 8 Examining diabetic subjects on their correlation with TTH and CAD: a statistical approach on exploratory results -- 8.1 Introduction -- 8.1.1 General application procedure -- 8.1.2 Medicinal imaging -- 8.1.2.1 Therapeutic photography and connected imaging methods for positron emission tomography (PET) -- 8.1.2.2 How is medical imaging used in digital health? -- 8.1.2.3 Biomedical image and analysis -- 8.1.3 Big data and Internet of Things -- 8.1.4 Artificial intelligence (AI) and machine learning (ML) -- 8.1.4.1 Artificial intelligence -- 8.1.4.2 Machine learning -- 8.1.4.2.1 Utilization of machine intelligence in healthcare -- 8.1.5 Big data and IoT applications in healthcare -- 8.1.6 Diabetes and its types -- 8.1.6.1 After effects of diabetes -- 8.1.6.2 Diabetes and headache -- 8.1.6.3 Obesity and overweight -- 8.1.7 Coronary artery disease (CAD) -- 8.1.7.1 Treatment -- 8.1.7.2 Insulin -- 8.1.7.3 Hypertension -- 8.1.7.4 Counteractive action -- 8.2 Review of literature -- 8.3 Research methodology -- 8.3.1 Trial setup -- 8.4 Result analysis and discussion -- 8.4.1 TTH cannot be -- 8.5 Originality in the presented work -- 8.6 Future scope and limitations -- 8.7 Recommendations and considerations -- 8.8 Conclusion -- References. , 9 Cancer prediction and diagnosis hinged on HCML in IOMT environment -- 9.1 Introduction to machine learning (ML) -- 9.1.1 Some machine learning methods -- 9.1.2 Machine learning -- 9.2 Introduction to IOT -- 9.3 Application of IOT in healthcare -- 9.3.1 Redefining healthcare -- 9.4 Machine learning use in health care -- 9.4.1 Diagnose heart disease -- 9.4.2 Diabetes prediction -- 9.4.3 Liver disease prediction -- 9.4.4 Surgery on robots -- 9.4.5 Detection and prediction of cancer -- 9.4.6 Treatment tailored -- 9.4.7 Discovery of drugs -- 9.4.8 Recorder of intelligent digital wellbeing -- 9.4.9 Radiology machine learning -- 9.4.10 Study and clinical trial -- 9.5 Cancer in healthcare -- 9.5.1 Methods -- 9.5.2 Result -- 9.6 Breast cancer in IoHTML -- 9.6.1 Study of breast cancer using the adaptive voting algorithm -- 9.6.2 Software development life cycle (SDLC) -- 9.6.3 Parts of undertaking duty PDR and PER -- 9.6.4 Info structure -- 9.6.5 Input stage -- 9.6.6 Output design -- 9.6.7 Responsible developers overview -- 9.6.8 Data flow -- 9.6.9 Cancer prediction of data in different views -- 9.6.10 Cancer predication in use case view -- 9.6.11 Cancer predication in activity view -- 9.6.12 Cancer predication in class view -- 9.6.13 Cancer predication in state chart view -- 9.6.14 Symptoms of breast cancer -- 9.6.15 Breast cancer types -- 9.7 Case study in breast cancer -- 9.7.1 History and assessment of patients -- 9.7.2 Recommendations for diagnosis -- 9.7.3 Discourse -- 9.7.4 Outcomes of diagnosis -- 9.8 Breast cancer algorithm -- 9.9 Conclusion -- References -- 10 Parameterization techniques for automatic speech recognition system -- 10.1 Introduction -- 10.2 Motivation -- 10.3 Speech production -- 10.4 Data collection -- 10.4.1 Recording procedure -- 10.4.2 Noise reduction -- 10.5 Speech signal processing -- 10.5.1 Sampling and quantization. , 10.5.2 Representation of the signal in time and frequency domain.