Visual interactive system (VIS) has been received significant attention for solving various complex problems. However, designing and implementing a novel VIS with the large scale of data is a challenging task. While existing studies have applied various visual analytics (VA) to analyze and visualize insightful information, deep visual analytics (DVA) have considered as a promising technique to provide input evidences and explain system results. In this study, we present several deep learning (DL) techniques for analyzing data with visualization, which summarizes the state-of-the-art review on (i) big data analysis, (ii) cognitive and perception science, (iii) customer behavior analysis, (iv) natural language processing, (v) recommended system, (vi) healthcare analysis, (vii) fintech ecosystem, and (viii) tourism management. We present open research challenges for emerging DVA in the visualization community. We also highlight some key themes from the existing literature that may help to explore for future study. Thus, our goal is to help readers and researchers in DL and VA to understand key aspects in designing VIS for analysing data.

Background: Diabetes is a long-term disease, which is characterised by high blood sugar and has risen as a public health problem worldwide. It may prompt a variety of serious illnesses, including stroke, kidney failure, and heart attacks. In 2014, diabetes affected approximately 422 million people worldwide and it is expected to hit 642 million people in 2040. The aim of this study is to analyse the effect of demographical and clinical characteristics for diabetics disease in Bangladesh. Methods: This study employs the quantitative approach for data analysis. First, we analyse differences in variables between diabetic patients and controls by independent two-sample t-test for continuous variables and Pearson Chi-square test for categorical variables. Then, logistic regression (LR) identifies the risk factors for diabetes disease based on the odds ratio (OR) and the adjusted odds ratio (AOR). Results: The results of the t-test and Chi square test identify that the factors: residence, wealth index, education, working status, smoking status, arm circumference, weight and BMI group show statistically (p < 0.05) significant differences between the diabetic group and the control group. And, LR model demonstrates that 2 factors (“working status” and “smoking status”) out of 13 are the significant risk factors for diabetes disease in Bangladesh. Conclusions: We believe that our analysis can help the government to take proper preparation to tackle the potentially unprecedented situations in Bangladesh.

Background: Diabetes is a long-term disease characterized by high blood sugar and has risen as a public health problem globally. Exploring and analyzing diabetes data is a timely concern because it may prompt a variety of serious illnesses, including stroke, kidney failure, heart attacks, etc. Several existing pieces of research have revealed that diabetes data, such as systolic blood pressure (SBP), diastolic blood pressure (DBP), weight, height, age, etc., can provide insightful information about patients diabetes states. However, very few studies have focused on visualizing diabetes mellitus (DM) insights to support healthcare administrator (HA)'s goals adequately, such as (i) decision-making, (ii) identifying and grouping associated factors, and (iii) analyzing large data effectively remains unexplored.Objective: This study aims to design an interactive visual system (Vis) to explore diabetes mellitus (DM) insights and its associated factors in Bangladesh. Methods:In this study, first, a case study method has employed to understand diabetes data. Second, we examine the potential of user-centered technology in addressing these challenges and design a Vis named "DiaVis" to process and present raw data in the form of graphics, graphs, and processed text, as well as a variety of user interaction possibilities. It helps to extract valuable data and present it in a simple and easy-to-understand way. Moreover, we highlight some key insights from our study that may help explore the healthcare community.Results: A user study with 20 individuals is used to evaluate our system. By allowing iterative exploration and modification of data in a dashboard with multiple-coordinated views, the DiaVis system improves the flow of visual analysis. Conclusion:This study suggests that the healthcare community should pay more attention to developing appropriate policy measures to reduce the risk of DM.