Use of IoT and Different Biofeedback to Measure TTH

This chapter applied the random sampling in selection of the subjects suffering with headache, and care was taken that they ensure to fulfill the International Headache Society criteria. Subjects under consideration were assigned the two groups of GSR-integrated audio-visual feedback, GSR (audio-visual)- and EMG (audio-visual)-integrated feedback groups. In 10 sessions, the subjects experienced the GSR and EMG BF therapy for 15 minutes. Twenty subjects were subjected to EEG therapy. The variables for stress (pain) and SF-36 (quality of life) scores were recorded at starting point, 30 days, and 90 days after the starting of GSR and EMG-BF therapy. To reduce the anxiety and depression in day-to-day routine, the present research work is shown as evidence in favor of the mindful meditation. The physical, mental, and total scores increased over the time duration of SF-36 scores after 30- and 90-days recordings (p<0.05). Intergroup analysis has demonstrated the improvement. EMG-audio visual biofeedback group also showed highest improvement in SF-36 scores at first and third month follow up. EEG measures the Alpha waves for the subjects after meditation. GSR, EMG, and EEG-integrated auditory-visual biofeedback are efficient in solution of stress due to TTH with most advantage seen.

Genetic Data Analysis

Genome-wide association studies (GWAS) or genetic data analysis is used to discover common genetic factors which influence the health of human beings and become a part of a disease. The concept of using genomics has increased in recent years, especially in e-healthcare. Today there is huge improvement required in this field or genomics. Note that the terms genomics and genetics are not similar terms here. Basically, the human genome is made up of DNA, which consists of four different chemical building blocks (called bases and abbreviated A, T, C, and G). Based on this, we differentiate each and every human being living on earth. The term ‘genetics' originated from the Greek word ‘genetikos'. It means ‘origin'. In simple terms, genetics can be defined as a branch of biology, which deals with the study of the functionalities and composition of a single gene in an organism. There are mainly three branches of genetics, which include classical genetics, molecular genetics, and population genetics.

ECG Image Classification Using Deep Learning Approach

Cardiovascular diseases are a major cause of death worldwide. Cardiologists detect arrhythmias (i.e., abnormal heart beat) with the help of an ECG graph, which serves as an important tool to recognize and detect any erratic heart activity along with important insights like skipping a beat, a flutter in a wave, and a fast beat. The proposed methodology does ECG arrhythmias classification by CNN, trained on grayscale images of R-R interval of ECG signals. Outputs are strictly in the terms of a label that classify the beat as normal or abnormal with which abnormality. For training purpose, around one lakh ECG signals are plotted for different categories, and out of these signal images, noisy signal images are removed, then deep learning model is trained. An image-based classification is done which makes the ECG arrhythmia system independent of recording device types and sampling frequency. A novel idea is proposed that helps cardiologists worldwide, although a lot of improvements can be done which would foster a “wearable ECG Arrhythmia Detection device” and can be used by a common man.

Diagnosis and Prognosis of Ultrasound Fetal Growth Analysis Using Neuro-Fuzzy Based on Genetic Algorithms

Acquisition of the standard plane is the prerequisite of biometric measurement and diagnosis during the ultrasound (US) examination. Based upon the analysis of existing algorithms for the automatic fetal development measurement, a new algorithm known as neuro-fuzzy based on genetic algorithm is developed. Firstly, the fetal ultrasound benchmark image is auto-pre-processed using normal shrink homomorphic technique. Secondly, the features are extracted using gray level co-occurrence matrix (GLCM), grey level run length matrix (GLRLM), intensity histogram (IH), and rotation invariant moments (IM). Thirdly, neuro-fuzzy using genetic approach is used to distinguish among the fetus growth as abnormal or normal. Experimental results using benchmark and live dataset demonstrate that the developed method achieves an accuracy of 97% as compared to the state-of-the-art methods in terms of parameters such as sensitivity, specificity, recall, f-measure, and precision rate.

Artificial Bee Colony-Based Associative Classifier for Healthcare Data Diagnosis

Data mining is likely to explore hidden patterns from the huge quantity of data and provides a way of analyzing and categorizing the data. Associative classification (AC) is an integration of two data mining tasks, association rule mining, and classification which is used to classify the unknown data. Though association rule mining techniques are successfully utilized to construct classifiers, it lacks in generating a small set of significant class association rules (CARs) to build an accurate associative classifier. In this work, an attempt is made to generate significant CARs using Artificial Bee Colony (ABC) algorithm, an optimization technique to construct an efficient associative classifier. Associative classifier, thus built using ABC discovered CARs achieve high prognostic accurateness and interestingness value. Promising results were provided by the ABC based AC when experiments were conducted using health care datasets from the UCI machine learning repository.

Machine Learning Perspective in Cancer Research

Advancement in genome sequencing technology has empowered researchers to think beyond their imagination. Researchers are trying their hard to fight against various genetic diseases like cancer. Artificial intelligence has empowered research in the healthcare sector. Moreover, the availability of opensource healthcare datasets has motivated the researchers to develop applications which can help in early diagnosis and prognosis of diseases. Further, next-generation sequencing (NGS) has helped to look into detailed intricacies of biological systems. It has provided an efficient and cost-effective approach with higher accuracy. The advent of microRNAs also known as small noncoding genes has begun the paradigm shift in oncological research. We are now able to profile expression profiles of RNAs using RNA-seq data. microRNA profiling has helped in uncovering their relationship in various genetic and biological processes. Here in this chapter, the authors present a review of the machine learning perspective in cancer research.

Prediction Models

Having/living convenient life through smart devices, people are more interested or more dependent on to predict something for future (i.e., with respect to their health, business, etc.). For that, many prediction models by several researchers are being used in many applications. Due to a vast (rapid) change in lifestyle, people are more prone to a number of life-threatening diseases when it comes to their well-being. Many of these diseases start developing their symptoms in their early stages. But, still many of these diseases like cancer, kidney damages remain unidentified in their developing stages. The earlier a disease is predicted, the easier it becomes to cure it and even prevent it. Predictive modeling provides a huge step forward in medical science in preventing the risk among patients. Prediction modeling is the process of analyzing current conditions to predict future results.

Pre-Processing Highly Sparse and Frequently Evolving Standardized Electronic Health Records for Mining

EHRs aid in maintaining longitudinal (lifelong) health records constituting a multitude of representations in order to make health related information accessible. However, storing EHRs data is non-trivial due to the issues of semantic interoperability, sparseness, and frequent evolution. Standard-based EHRs are recommended to attain semantic interoperability. However, standard-based EHRs possess challenges (in terms of sparseness and frequent evolution) that need to be handled through a suitable data model. The traditional RDBMS is not well-suited for standardized EHRs (due to sparseness and frequent evolution). Thus, modifications to the existing relational model is required. One such widely adopted data model for EHRs is entity attribute value (EAV) model. However, EAV representation is not compatible with mining tools available in the market. To style the representation of EAV, as per the requirement of mining tools, pivoting is required. The chapter explains the architecture to organize EAV for the purpose of preparing the dataset for use by existing mining tools.

ACO_NB-Based Hybrid Prediction Model for Medical Disease Diagnosis

In recent years, a huge increase in the demand of medically related data is reported. Due to this, research in medical disease diagnosis has emerged as one of the most demanding research domains. The research reported in this chapter is based on developing an ACO (ant colony optimization)-based Bayesian hybrid prediction model for medical disease diagnosis. The proposed model is presented in two phases. In the first phase, the authors deal with feature selection by using the application of a nature-inspired algorithm known as ACO. In the second phase, they use the obtained feature subset as input for the naïve Bayes (NB) classifier for enhancing the classification performances over medical domain data sets. They have considered 12 datasets from different organizations for experimental purpose. The experimental analysis advocates the superiority of the presented model in dealing with medical data for disease prediction and diagnosis.

Strategic Analysis in Prediction of Liver Disease Using Different Classification Algorithms

Liver diseases avert the normal activity of the liver. Discovering the presence of liver disorder at an early stage is a complex task for the doctors. Predictive analysis of liver disease using classification algorithms is an efficacious task that can help the doctors to diagnose the disease within a short duration of time. The main motive of this study is to analyze the parameters of various classification algorithms and compare their predictive accuracies so as to find the best classifier for determining the liver disease. This chapter focuses on the related works of various authors on liver disease such that algorithms were implemented using Weka tool that is a machine learning software written in Java. Also, orange tool is utilized to compare several classification algorithms in terms of accuracy. In this chapter, random forest, logistic regression, and support vector machine were estimated with an aim to identify the best classifier. Based on this study, random forest with the highest accuracy outperformed the other algorithms.