# Data Analytics in Smart Healthcare: The Recent Developments and Beyond
## 2 Special Issue Articles
The results are around 0.7 (c-index) which has proven the feasibility of the application. The manuscript \"Data analysis and forecasting of tuberculosis prevalence rates for smart healthcare based on a novel combination model\" authored by [PERSON], [PERSON] and [PERSON] proposed a combination forecasting model to determine the tuberculosis prevalence rate [10]. The steps have been divided into five major parts, including the Kruskal-Wallist test (also known as one-way analysis of variance (ANOVA) on ranks), regression model, cuckoo search optimization algorithm, combine forecasting method with weighted coefficients, and radial basis function neural networks. The goodness-of-fit with adjusted R-squares is more than 0.96. [PERSON] and [PERSON] studied the association between policy factors and healthcare system efficiency in their manuscript entitled \"Estimation of association between healthcare system efficiency and policy factors for public health\" [11]. The efficiency score was corrected by data envelopment analysis with bootstrapping. It was then investigated with policy factors using the Tobit model. Results have concluded that the efficiency was affected by user choice for basic insurance coverage and degree of decentralization to sub-national governments in a reverse positive way. \"A systematic review of open source clinical software on GitHub for improving software reuse in smart healthcare\" authored by [PERSON] and [PERSON] [12] collected and analyzed around 13,000 GitHub repositories on open source clinical software between 2009 and 2018. Some representative findings were highlighted (i) popularity of software using number of stars; (ii) most productive countries contributing to the community; (iii) the causes of the popularity of the software; and (iv) top 10 groups of software. In [13], [PERSON], [PERSON], [PERSON], and [PERSON] wrote a manuscript \"Relationship between continuity of care in the multidisciplinary treatment of patients with diabetes and their clinical results\". This paper studied the relationship between continuity of care in the multidisciplinary treatment of type 2 diabetes sufferers and their clinical results. The continuity was measured by four traditional parameters named sequential continuity, Herfindahl index, continuity of care index and usual provider continuity. The result revealed that continuity of care by dietitians, physicians and nurses has a positive effect on the clinical results of type 2 diabetes patients. In particular, authors had emphasized the importance of dietitians and nurses as they may be treated as less important in some clinics and hospitals. [PERSON], [PERSON], [PERSON], and [PERSON] presented a manuscript entitled \"Statistical edge detection and circular hough transform for optic disk localization\" [14]. An optic disk detection algorithm is proposed for the application in retinal images. Two techniques were employed for the detection which were the statistical edge detection algorithm and circular hough transformation. This can be applied to various retinal diseases like glaucoma, papilledema and diabetic retinopathy. About 260 samples were tested on the proposed algorithm which had an error rate of less than 3%. A combined deep convolutional neural network and support vector machine was adopted for the classification of lung cancers via pulmonary computed tomography (CT) images in the article \"Classification of pulmonary CT images by using hybrid 3D-deep convolutional neural network architecture\" [15], authored by [PERSON], and [PERSON]. The accuracy was 92% over 2100 testing cases using the Data Science Bowl and Kaggle dataset. It also recommended introducing more convolution layers in order to extract more representative features for better accuracy. [PERSON] et al. in their manuscript entitled \"Probability analysis of hypertension-related symptoms based on XGBoost and clustering algorithm\" [16] proposed a clustering based XGBoost algorithm to classify type I and type II hypertension.