A Literature Review on Thyroid Hormonal Problems in Women Using Data Science and Analytics

Currently, thyroid disorders are more common and widespread among women worldwide. In India, seven out of ten women are suffering from thyroid problems. Various research literature studies predict that about 35% of Indian women are examined with prevalent goiter. It is very necessary to take preventive measures at its early stages, otherwise it causes infertility problem among women. The recent review discusses various analytics models that are used to handle different types of thyroid problems in women. This chapter is planned to analyze and compare different classification models, both machine learning algorithms and deep leaning algorithms, to classify different thyroid problems. Literature from both machine learning and deep learning algorithms is considered. This literature review on thyroid problems will help to analyze the reason and characteristics of thyroid disorder. The dataset used to build and to validate the algorithms was provided by UCI machine learning repository.

Gait Abnormality Detection Using Deep Convolution Network

Human gait analysis plays a significant role in clinical domain for diagnosis of musculoskeletal disorders. It is an extremely challenging task for detecting abnormalities (unsteady gait, stiff gait, etc.) in human walking if the prior information is unknown about the gait pattern. A low-cost Kinect sensor is used to obtain promising results on human skeletal tracking in a convenient manner. A model is created on human skeletal joint positions extracted using Kinect v2 sensor in place using Kinect-based color and depth images. Normal gait and abnormal gait are collected from different persons on treadmill. Each trial of gait is decomposed into cycles. A convolutional neural network (CNN) model was developed on this experimental data for detection of abnormality in walking pattern and compared with state-of-the-art techniques.

Data Analysis in Context-Based Statistical Modeling in Predictive Analytics

Data analysis is a process of studying, removing non-required data in the view level, and converting to needed patterns for sub decisions to make an aggregated decision. Statistical modeling is the process of applying statistical techniques in data analysis for taking proactive decisions depend requirements. The statistical modeling identifies relationship between variables, and it encompasses inferential statistics for model validation. The focus of the chapter is to analyze statistical modeling techniques in different contexts to understand the mathematical representation of data. The correlation and regression are used for analyzing association between key factors of companies' activities. Especially in business, correlation describes positive and negative correlation variables for analyzing the factors of business for supporting the decision-making process. The key factors are related with independent variables and dependent variables, which create cause and effect models to predict the future outcomes.

A Survey on Intelligence Tools for Data Analytics

The recent development in artificial intelligence is quite astounding in this decade. Especially, machine learning is one of the core subareas of AI. Also, ML field is an incessantly growing along with evolution and becomes a rise in its demand and importance. It transmogrified the way data is extracted, analyzed, and interpreted. Computers are trained to get in a self-training mode so that when new data is fed they can learn, grow, change, and develop themselves without explicit programming. It helps to make useful predictions that can guide better decisions in a real-life situation without human interference. Selection of ML tool is always a challenging task, since choosing an appropriate tool can end up saving time as well as making it faster and easier to provide any solution. This chapter provides a classification of various machine learning tools on the following aspects: for non-programmers, for model deployment, for Computer vision, natural language processing, and audio for reinforcement learning and data mining.

Information Technology Act 2000 and the Potential Use of Data Analytics in Reducing Cybercrime in India

Cybercrime is increasing rapidly in this digitized world. Be it business, education, shopping, or banking transactions, everything is on cyberspace. Cybercrime covers a wide range of different attacks such as financial cybercrime, spreading computer viruses or malware, internet fraud, pornography cybercrime, intellectual property rights violation, etc. Due to increased cyber-attacks these days, the online users must be aware of these kinds of attacks and need to be cautious with their data online. Each country has their own laws for dealing with cybercrime. The different measures taken by the government of India to combat cybercrime are explained in this chapter. How the potential use of data analytics can help in reducing cybercrime in India is also explained.

Healthcare Conversational Chatbot for Medical Diagnosis

Medical services are basic needs for human life. There are times when consulting a doctor can be difficult. The proposed idea is an AI-based chatbot that will provide assistance to the users regarding their health-based issues. The state of the art in the aforementioned field includes extractive bots that extract the keywords (i.e., symptoms from the user's input) and suggest its diagnosis. The proposed idea will be a conversational bot, which unlike the QnA bot will take into consideration the context of the user's whole conversation and reply accordingly. Thus, along with symptom extraction, the user will get a better experience conversing with the bot. The user can also normally chat with the chatbot for issues like if the user is not emotionally sound. For example, the bot will console the user if he/she is feeling stressed by recognizing the emotional health of the user.

Machine Learning and Its Application in Monitoring Diabetes Mellitus

Data science is a fast-growing area that deals with data from its origin to the knowledge exploration. It comprises of two main subdomains, data analytics for preparing data, and machine learning to probe into this data for hidden patterns. Machine learning (ML) endows powerful algorithms for the automatic pattern recognition and producing prediction models for the structured and unstructured data. The available historical data has patterns having high predictive value used for the future success of an industry. These algorithms also help to obtain accurate prediction, classification, and simulation models by eliminating insignificant and faulty patterns. Machine learning provides major advancement in the healthcare industry by assisting doctors to diagnose chronic diseases correctly. Diabetes is one of the most common chronic disease that occurs when the pancreas cells are damaged and do not secrete sufficient amount of insulin required by the human body. Machine learning algorithms can help in early diagnosis of this chronic disease by studying its predictor parameter values.

Clustering by K-Means Method and K-Medoids Method

Data science and data analytics are becoming increasingly important. It is widely used in scientific and real-life applications. These methods enable us to analyze, understand, and interpret the data in every field. In this study, k-means and k-medoids clustering methods are applied to cluster the Statistical Regions of Turkey in Level 2. Clustering analyses are done for 2017 and 2018 years. The datasets consist of “Distribution of expenditure groups according to Household Budget Survey” 2017 and 2018 values, “Gini coefficient by equivalised household disposable income” 2017 and 2018 values, and some features of “Regional Purchasing Power Parities for the main groups of consumption expenditures” 2017 values. Elbow method and average silhouette method are applied for the determining the number of the clusters at the beginning. Results are given and interpreted at the conclusion.

Influence of Some Sociodemographic Factors on Causes of Death Among South African Youth

Youth mortality is a challenge in South Africa, where on a daily basis a number of deaths are reported and are related to youth. This study used the 2014 Statistics South Africa data to examine the influence of sociodemographic factors on causes of death among South African youth aged 15-34 years, using a logistic regression model. The results showed that there is a significant relationship between education and causes of death as well as other sociodemographic factors and that the youth mortality will likely reduce if more youth have higher levels of education. The results of this study could be used to improve national prevention campaigns to reduce death among young South Africans, especially adolescents.

Computer-Aided Diagnosis of Knee Osteoarthritis From Radiographic Images Using Random Forest Classifier

Knee osteoarthritis (OA) is a degenerative joint disease that occurs due to wear down of cartilage. Early diagnosis has a pivotal role in providing effective treatment and in attenuating further effects. This chapter aims to grade the severity of knee OA into three classes, namely absence of OA, mild OA, and severe OA, from radiographic images. Pre-processing steps include CLAHE and anisotropic diffusion for contrast enhancement and noise reduction, respectively. Niblack thresholding algorithm is used to segment the cartilage region. GLCM features like contrast, correlation, energy, homogeneity, and cartilage features such as area, medial, and lateral thickness are extracted from the segmented region. These features are fed to random forest classifier to assess the severity of OA. Performance of random forest classifier is compared with ANFIS and Naïve Bayes classifier. The classifiers are trained with 120 images and tested with 45 images. Experimental results show that random forest classifier achieves a higher accuracy of 88.8% compared to ANFIS and Naïve Bayes classifier.