# Data Analytics in Smart Healthcare: The Recent Developments and Beyond
## 2 Special Issue Articles  
This section provides a summary using both table (Table 1) and written description. The first paper entitled \"Comparing deep learning and classical machine learning approaches for predicting inpatient violence incidents from clinical text\" coauthored by [PERSON], [PERSON] and [PERSON], focused on the topic of predicting inpatient violence incidents [7]. Deep learning and shallow learning have been applied and compared to the clinical text to build the classification model. Results showed that the improvement by the deep learning approach is statistically insignificant using the area under the receiver operating characteristic curve. It recommends the selection between deep learning and shallow learning, and the former allows more experimentation in model setup whereas the latter possesses a small training time. [PERSON], [PERSON], [PERSON], and [PERSON] proposed a random forest-based algorithm for the prediction of Type 2 Diabetes and hypertension using risk-factors from individuals [8] in their manuscript \"Hybrid prediction model for Type 2 Diabetes and hypertension using DBSCAN-based outlier detection, synthetic minority over sampling technique (SMOTE)\". Two techniques named density-based spatial clustering and synthetic minority over-sampling have been utilized for outlier removal and imbalanced classes handling. This showed significant improvement in precision, sensitivity, specificity, F1 score and accuracy, compared to existing methods support vector machine (SVM), multilayer perception (MLP), logistic regression, naive bayes, and C4.5 using three benchmark datasets. \begin{table}
\begin{tabular}{c|c|c} \hline \hline
**Work** & **Application** & **Methodology** \\ \hline
[7] & Prediction of inpatient violence incidents & Recurrent neural network; convolutional neural network; neural network; Naive Bayes; support vector machine; decision tree \\ \hline
[8] & Prediction of type 2 diabetes and hypertension & density-based spatial clustering; synthetic minority over-sampling \\ \hline
[9] & Prediction of biochemical recurrences in patients treated by stereotactic body radiation therapy & prostate clinical outlook \\ \hline
[10] & Forecast of tuberculosis prevalence rate & Kruskal-Wallist test; regression model; Cuckoo search optimization algorithm; radial basis function neural networks \\ \hline
[11] & Investigation of the association between policy factors and healthcare system efficiency & Tobit model \\ \hline
[12] & Improvement on software reuse in smart healthcare & Systematic analysis \\ \hline
[13] & Investigation of the relationship between continuity of care in the multidisciplinary treatment of patients with diabetes and their clinical results & Statistical analysis \\ \hline
[14] & Optic disk localization & Statistical edge detection; circular hough transform \\ \hline
[15] & Classification of lung cancers & Deep convolutional neural network; support vector machine \\ \hline
[16] & Probability analysis of hypertension-related symptoms & XGBoost; clustering algorithm \\ \hline
[17] & Skin aging estimation & Scale-invariant feature transform; color histogram intersection; polynomial regression; support vector regression \\ \hline
[18] & Minimizing the number of physicians and nurses & Discrete event simulations \\ \hline
[19] & Classification of organ inflammation & Genetic algorithm; support vector machine \\ \hline \end{tabular}
\end{table} 
Table 1: Summary on the application and methodology of the special issue articles. [PERSON] et al. [9] in their manuscript entitled \"The prostate clinical outlook (PCO) classifier application for predicting biochemical recurrences in patients treated by stereotactic body radiation therapy (SBRT)\", implemented a prostate clinical outlook classifier for the classification of biochemical recurrence in patients undergoing stereotactic body radiation therapy. It concluded that four parameters are effective in increasing the prediction accuracy: Gleason scores, pretreatment prostate specific antigen, clinical radiological staging and age.