can you write a conclusion or summary for the below table 
No	Title	Ref	Date	Aim	Data	ML algorithm	XAI 	Tools	Unit	Results	Limitations
1	An explainable machine learning framework for lung cancer hospital length of stay prediction.	(Alsinglawi et al., 2022)	2022	To explore the LOS prediction as a health assessment metric for resource utilization in the ICU settings with ML classification approaches.	MIMIC-III	Random Forest, XGBoost and Logistic Regression	SHAP	N/A	ICU	Random Forest is the best model with AUC of 98% 	- Only categorical prediction (long or short) stays, no precise number.
- Focusing only for lung cancer patients and only for ICU. 
- Model performance can be enhanced by using modern ML models such as neural networks. 
2	The prediction of hospital length of stay using unstructured data.	(Chrusciel et al., 2021)	2021	To assess the performance improvement for machine learning-based hospital length of stay (LOS) predictions when clinical signs written in text are accounted for and compared to the traditional approach of solely considering structured information such as age, gender, and major ICD diagnosis.	Dr Warehouse platform	Two Random Forest models	Features importance 	N/A	ED	The model using unstructured data had a 75.0% accuracy compared to 74.1% for the model containing structured data. 	- Not much discussed for XAI, only simple features importance technique. 
- Only discussed Random Forest model. 
- Accuracy is not high, model performance can be improved by using other models. 
3	Machine-learning prediction for hospital length of stay using a French medico-administrative database.	(Jaotombo et al., 2022)	2022	To explore Machine Learning (ML) models that best predict Prolonged Hospital Length of Stay (PLOS).	French Medico-Administrative database (PMSI)	Logistic Regression (LR), Classification and Regression Trees (CART), Random Forest (RF), Gradient Boosting (GB) and Neural Networks (NN)	Features importance	Python 3.7 with Sci Kit Learn 0.24.1 and Keras 2.4.0 	ED	Gradient Boosting (GB) with AUC of 0.8101	- Some of the variables are collected before or during hospitalization, whereas others are collected at or after discharge (different time sequence).
- Not much discussed for XAI, only simple featurea importance techniques.
- Model performance can be enhanced by using modern ML models such as neural networks.
4	Machine learning model for predicting the length of stay in the intensive care unit for Covid-19 patients in the eastern province of Saudi Arabia.	(Alabbad et al., 2022)	2022	Developing machine learning models to predict and estimate the number of days COVID-19 patients in Saudi Arabia may spend in the ICU unit.	King Fahad University hospital	Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Ensemble models	N/A	Python	ICU	Random Forest with accuracy of s 94.16%	- XAI is not implemented. 
- Limited only COVID-19 ICU patient which can’t be generalized. 
- Model performance can be enhanced by using modern ML models such as neural networks.
- Dataset is small, only 895 records. 


No	Title	Ref	Date	Aim	Data	ML algorithm	XAI 	Tools	Unit	Results	Limitations
5	Using Machine Learning Models to Predict the Length of Stay in a Hospital Setting.	(Mekhaldi et al., 2020)	2020	To apply ML to predict acute patient’s LOS in a hospital setting.	French Medico-Administrative database (PMSI)	Random Forest (RF) and the Gradient Boosting (GB)	N/A	Python and R	N/A	Both models R2 greater than 0.92 and MAE lower than 0.45	- XAI is not implemented. 
- Only tested with two models, not including the Neural Network. 
- Accuracy is not high, model performance can be improved by using other models.
6	Predicting length of stay in hospitals intensive care unit using general admission features.	(Abdelrazek et al., 2021)	2021	Proposed a framework for predicting patient LOS in the ICU using different machine learning (ML) techniques.	Different sources, EMR, doctor’s and nurse’s sheets, laboratory results. 	Neural Network (NN), Classification, Regression Tree, Tree Bagger, Random Forest, Support Vector Machine, KNN, Naïve Baye and Fuzzy Rules 	N/A	MATLAB	ICU	Fuzzy Rules with accuracy reach 92%.	- XAI is not implemented. 
- MATLAB is not the best option for ML modeling when we need to deploy later.

7	Predicting ICU Length of Stay for Patients with Diabetes Using Machine Learning Techniques.	(Hu et al., 2022)	2022	To predict the length of stay for patients with diabetes by applying machine learning techniques on clinical data available during the first 8 hours of ICU admissions.	MIMIC-III	k-nearest neighbors (KNN), decision tree (DT), Random Forest (RF), Gradient Boosting (GB), and neural network (NN), linear regression (LR) and ridge regression (RR), logistic regression (LR) and support vector machine (SVM)	N/A	Python	ICU	The Nural Network model achieved the best performance with an R2 value 0.3969 and a mean absolute error 1.94 days.	- XAI is not implemented. 
- Limited to Diabetes patients.
- R2 value is very low with a high mean absolute error rate.

8	Machine Learning Models to Predict Length of Stay In Hospitals.	(Jain et al., 2022)	2022	To develop machine learning models to predict the hospital length of stay from open healthcare data.	Statewide Planning and Research Cooperative System (SPARCS)	Decision Trees, Random Forests, and XGBoost.	Features importance	N/A	N/A	XGBoost, with an R2 value of 0.44.	- Not much discussed for XAI, only simple features importance technique. 
- R2 value is very low.
9	A Machine Learning Approach for Predicting Patient’s Length of Hospital Stay with Random Forest Regression.	(Suha & Sanam, 2022)	2022	Proposes a machine learning approach for analyzing patient data and building a reliable prediction model incorporating Random Forest Regression Model to estimate the length of a patient’s stay in the hospital.	Statewide Planning and Research Cooperative System (SPARCS)	Linear Regressor, Decision Tree, SVM Regressor, Bayesian Ridge, Linear Lasso, Gradient-Boosting, Random Forest, Adaptive Boosting, Multilayer Perceptron and Deep Neural Network.	PCA and Chi-square	Google Colab, Python, Scikit-learn and Tensorflow	N/A	Random Forest with MAE of 1.	- Not mentioned the features (variables) contribute to the prediction model.

No	Title	Ref	Date	Aim	Data	ML algorithm	XAI 	Tools	Unit	Results	Limitations
10	Hospital Length of Stay Prediction with Ensemble Methods in Machine Learning	(Zheng et al., 2021)	2021	To develop a machine learning system to predict LOS of patients at the admission phase, with initial diagnosis and test results.	Statewide Planning and Research Cooperative System (SPARCS)	Random Forests, Gradient Boosting, Adaptive Boosting, and Deep Neural Networks.	N/A	N/A	N/A	Random forest with accuracy of 75.64%. 	- XAI is not implemented. 
- The best accuracy is not high. 
11	Length of Stay Prediction in Acute Intensive Care Unit in Cardiothoracic Surgery Patients.	(Mollaei et al., 2021)	2021	To apply machine learning (ML) methods to predict the Length of Stay in an Intensive Care Unit (LOS-ICU) based on preoperative factors.	Portuguese hospital in Lisbon, Portugal Dataset	Logistic Regression, Gradient Boosting, Random Forest, Support Vector Machine (SVM) and AdaBoost model	N/A	N/A	ICU	Random forest with accuracy of 76%. 	- It is limited to the cardiothoracic surgery service.
- XAI is not implemented. 
- The best accuracy is not high.

12	Predicting Hospital Length of Stay of Neonates Admitted to the NICU Using Data Mining Techniques	(Mansouri et al., 2020)	2020	To predict the length of stay of newborns in NICU using data mining techniques. 	MIMIC-III	Gradient Boosting Regressor, Random Forest Regressor, XGBoost Regressor, KNN Regressor, Linear Regression	SHAP	N/A	NICU	Random Forest with an R2 score of 0.78. 	- Model performance can be enhanced by using modern ML models such as neural networks.

13	Predicting Length of Stay for Cardiovascular Hospitalizations in the Intensive Care Unit: Machine Learning Approach	(Alsinglawi et al., 2020)	2020	To introduces a predictive research architecture to predict Length of Stay (LOS) for heart failure diagnoses from electronic medical records using the state-of-art machine learning models, in particular, the ensembles regressors and deep learning regression models.	MIMIC-III	Random Forest Regressor, Gradient Boosting Regressor, Deep Neural Networks, Stacking Regressor	Features importance	Python with Scikit-learn and Keras	ICU	Gradient Boosting Regressor with R2 score of 0.81	- It is limited to Cardiovascular patients. 
- Not much discussed for XAI, only simple features importance technique. 

14	Machine Learning-based Prediction of Prolonged Length of Stay in Newborns	(Thompson et al., 2018)	2018	To develop the application of machine learning-based prediction of a Healthcare Cost and Utilization Project (HCUP) dataset and reported on the performance of various developed predictive models.	HCUP State Inpatient Databases (SID)	Naive Bayes, Logistic Classifier, Multi-layer Perceptron (MLP), Simple Logistic, Support Vector Machines (SVM), Decision Tree (DT), Random Forest, and Random Tree.	N/A	Weka	NICU	Random Forest with ROC of 0.877
	- XAI is not implemented. 
- Weka is not the best option for ML modeling when we need to deploy later