# A Hybrid Intelligent Approach to Predict Discharge Diagnosis in Pediatric Surgical Patients
## 1 Introduction
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**Problem** & **Proposal** & **Results** & **Validation** & **Reference** \\ \hline Classification of unhealthy and healthy neonates & The authors introduced a system for classifying unhealthy and healthy neonates in networks (ANN). & ANN & Accuracy: 98.42\%, & 10-fold cross validation & [PERSON] et al. [20] \\ Disease diagnosis (tuberculosis, malaria, and intestinal parasites) & The authors introduced a system based on convolutional neural networks (CNN) for disease diagnosis from microscopic images. & CNN & Accuracy: 96.05\% & Data splitting & [PERSON] et al. [21] \\ Detection of congenital abnormalities of the kidney and urinary treatment, and (SVM) techniques. & The authors introduced a computer-aided diagnosis (CAD) system based on ultrasound imaging data, which consists of 3 components, namely: (1) kidney segmentation, (2) feature extraction, and (3) A classification model based on the support vector machines (SVM) technique. & CNN and SVM & AUCs \(>\) 0.88 & 10-fold cross-validation & [PERSON] et al. [22] \\ Mortality prediction & In this study, the authors developed a model based on the naive Bayes (NB) technique to predict in-hospital mortality in patients undergoing transcatheter mitral valve repair. & NB & AUC: 0.83 & Data splitting & [PERSON] et al. [23] \\ Detection of hypertension & The authors presented a CAD system to help clinicians in the hypertension prediction, bagging & CNN and bagging & Accuracy: 90.90\% & 10-fold cross-validation & [PERSON] et al. [24] \\ Arrhythmia classification & A system for monitoring cardiac patients using machine learning techniques like probabilistic neural network (PNN) and multilayer perceptron neural network (MLPNN). & PNN & F-measure: 91.83 & 22-fold cross-validation & [PERSON] [25] \\ Voice pathology detection & The authors presented a method combining density clustering and support vector machines for two pathology detection. & SVM and DSPCA & Accuracy: 98.00\% & Cross validation & [PERSON] and [PERSON] [26] \\ Disease diagnosis (heart disease, Parkinson’s disease, and BUTs liver disorder) & A hybrid system for diseases diagnostic is proposed, which is compounded by a new method entitled k-medoids clustering-based attribute weighting (knAW) as a data pre-processing method, and an SVM was preferred in the classification phase. & SVM and RAN & Accuracy: 98.95\% & 10-fold cross-validation & [PERSON] [9] \\ Predicting patient revisits & This study focuses on the predictive identification of patients frequently revisiting the University of Virginia Health System Emergency Department. The authors proposed the use of the XCBost algorithm to predict the risk of revisit. & XGBoost & AUC: 0.754 & Data splitting & [PERSON] et al. [6] \\ Identification of brain tumors & The detection of tumors is performed with the help of automatic computing technique. & CNN & Accuracy: 99.1\% & Data splitting & [PERSON] et al. [27] \\ \hline \end{tabular}
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Table 1: Representative publications that report the use of machine learning approaches for medical diagnosis.