Emergence of IoT and Big Data

The main focus of this research paper is to understand the use of IoT in healthcare to achieve sustainable development. This paper has used the descriptive method to collect the data for study. The research methodology is FAHP, which is a cross-sectional research survey in nature. After data collection, the agreed paired comparison matrices, which are then further allocated to the weighted criteria and then accordingly a priority for the IoT usage is judged. According to the findings of the research, it has been identified that “profit maximization” and “quality of life” were on the top for priority for IoT in the healthcare sector for sustainable development, although, three major areas were identified. Additionally, if we consider according to usage then it is mostly used by the health care sector as UV radiation, dental care, and fall detection.

Effective and Accurate Diagnosis Using Brain Image Fusion

Medical imaging techniques are routinely employed to create images of the human system for clinical purposes. Multi-modality medical imaging is a widely used technology for diagnosis, detection, and prediction of various tissue abnormalities. This chapter is focused on the development of an improved brain image processing technique for the removal of noise from a magnetic resonance image (MRI) for accurate image restoration. Feature selection and extraction of MRI brain images are processed using image fusion. The medical images suffer from motion blur and noise for which image denoising is developed through non-local means (NLM) filtering for smoothing and shrinkage rule for sharpening. The peak signal to noise ratio (PSNR) of improved curvelet based self-similarity NLM method is better than discrete wavelet transform with an NLM filter.

Automation in Healthcare

Major challenges to the society are the people have aging populace and occurrence of continual diseases and eruption of transferable diseases. to embark upon these unmet healthcare desires for the quick guess and therapeutic of all the important diseases a new area called health informatics is emerging as an interdisciplinary research which is dealing with the getting hold of, spread, dispensation, to store as well retrieve. Particularly when the industry is acquired the health information by using the unassuming sense and wearable technology is well thought-out as groundwork stone in healthiness industry. According to a reports, sensors can be worn and hooked on clothes which can acquire the health information uninterrupted.

Elderly Behavior Prediction Using a Deep Learning Model in Smart Homes

The goal of a smart home is to keep track of the behaviors of the older adults with disabilities within the home, and then anticipate their activity to help with other actions. Elderly and disabled people have problems with their daily lives, while most other people are unaware of their difficulties. Helping the elderly to live independently allows them to lead their daily lives in a better manner. The implementation of analytics and machine learning algorithms leads to a predictive approach to health care services. In this chapter, a learning model in a smart home concept focuses on making it possible for the elderly to remain safe and comfortable at home. The transformative home security device learning architecture of the smart home platform is a valuable solution to studying mobility patterns at home, with the ability to identify behavioral changes related to issues of wellbeing. A predictive learning system can effectively recognize and identify the behavior of the elderly. A learning model, a recurrent neural network (RNN) is proposed to evaluate the people's activity. The focus of the present study is to forecast the deterioration in mental function and give warnings for the benefit of seniors.

Machine Learning Is the Future for Lung Cancer Prognosis and Prediction

Lung cancer is one of the major reasons for the death if it is not diagnosed in the early stages of cancer. It is the one among the most dreadful disease which affects in the lungs function. It can be identified only after the disease spread into the deeper parts of the lungs and then only it will make a life threading problem. Lung cancer prognosis which was done based on the various parameters such as age, sex, condition of smoking, duration of smoking and count of smoking per day. The proceedings were done using the algorithm for the time to first cigarette after awakening which is represented as TTFC. The expert doctor says that the back-propagation network is a great deal in the recognition of the lung cancer without any involvement by them. This research is based on the classification of lung cancer and its stages using the establishment of the BPN and predicts the recurrence. Similarly, with this BPN, an algorithm that is inspired from its habitat known as ant lion optimization algorithm is also used in the optimization of weights and parameters of the BPN. The use of the ALO algorithm provides an improved convergence mechanism by improving the proposed technique's accuracy. The use of this proposed method with the BPN optimizes the network and the ALO optimizer provides an accurate prediction of the lung cancer by the earlier stage and even predicts the changes for reoccurrence after diagnosis. The prognosis analysis was made by the various comparative study between the characteristic features of HIV and the unaffected person using the algorithm such as the Wilcoxon rank-sum test. This algorithm will continuously classify the viral load and CD4 count which is based on factors such as age, sex, and smoking activities. It will be useful for early diagnosis and future prediction. Lung cancer rates can be analyzed based on the incident rates of affected and unaffected persons to HIV infections.

Managing the Scarcity of Monitoring Data Through Machine Learning for Human Behavior in Mental Health Care

Nowadays, in many fields, including medicine and public health, developments in information and communication technology have introduced a revolution. Due to the absence of effective machinery algorithms and many possibilities to enhance patient understanding of chronic diseases, information gathered from the distinct sources were ignored. Therefore, researchers are investigating information shortages of computer training methods. This work recommends a fresh strategy to produce stronger predictive models specifically intended for the problems. In order to address the scarcity of data, works propose to make use of a semi-managed learning environment while using all unlabeled data sets. The techniques suggested were implemented in the mental health care field of bipolar disorder. The methods suggested enhancing the efficiency of ranking to the accuracy of ≈ 73% to ≈ 90%. The results have been achieved by overcoming previous methods that use traditional monitored methods of learning.

A Surrogate Data-Based Approach for Validating Deep Learning Model Used in Healthcare

IoT-based communication between medical devices has encouraged the healthcare industry to use automated systems which provide effective insight from the massive amount of gathered data. AI and machine learning have played a major role in the design of such systems. Accuracy and validation are considered, since copious training data is required in a neural network (NN)-based deep learning model. This is hardly feasible in medical research, because the size of data sets is constrained by complexity and high cost experiments. The availability of limited sample data validation of NN remains a concern. The prediction of outcomes on a NN trained on a smaller data set cannot guarantee performance and exhibits unstable behaviors. Surrogate data-based validation of NN can be viewed as a solution. In the current chapter, the classification of breast tissue data by a NN model has been detailed. In the absence of a huge data set, a surrogate data-based validation approach has been applied. The discussed study can be applied for predictive modelling for applications described by small data sets.

Disease Diagnosis and Treatment Using Deep Learning Algorithms for the Healthcare System

This chapter explicates deep learning algorithms for healthcare opportunities. Deep Learning is a group of neural network algorithms and learns from various levels of representation and abstraction to aid in the data interpretation. Since the datasets get bigger, computers become more powerful, and the training of the datasets (images or numeric) gets much easier and the results achieved using deep learning are better. In contrast to machine-learning algorithms that rely on large amounts of labelled data, human cognition can find structure in unlabeled data, a technique known as unsupervised learning. It was noted that using deep learning algorithms on the dataset will reduce the number of unnecessary biopsies in future. In this chapter, the authors study deep learning algorithms to diagnose diabetic retinopathy retinal images and training a convolution neural network (CNN) algorithm to identify object tumors from a large set of brain tumor images.

Real-Time Mental Health Monitoring

Mental disorders have been identified as one among the leading causes of the global disease burden. Despite being one of the first nations in the world to identify mental health as an important indicator of personal well-being and having adequate plans and policies for ensuring the same, one in every seven Indians is affected by mental disorders of varying severity. Through this manuscript we try to analyze how real-time mental health monitoring has helped improve productivity among the global workforce as well as prevented deterioration of individual mental health across the globe. Our main plan of this study is to identify the significant efforts in mental health monitoring across the globe and then chalk out a real-time mental health monitoring framework for India. We also propose a real-time mental health monitoring smartphone-based framework for India we name as SmartMHealth. We describe the basic components of this framework in this study itself.

Automation in Healthcare Services

The health care service industry (also known as a medical industry) is an industry that is comprised of the services related to the safeguarding or enhancement of patient health or provides services to treat patients with medicinal, protective, rehabilitative, and analgesic care. For the last two decades, it has been seen that there are drastic changes in healthcare services through automation, digitalization, technological innovation, and communication. Automation has made a revolutionary change in the healthcare industry and allowed for it to be more cost-effective for the industry to run day-to-day operations. Automation-driven health care activities are free from human fatigue and error, so they can help out to provide consistency, accuracy, and potentially lead to a reduction in patient complications, infections, and deaths. Besides, automation can help hospitals, professionals, and doctors for cost-reduction measures and increased efficiency as part of their monetary benefits.