1. Aim : Develop prediction model that can be used to facilitate clinicians in targeting patients at high or low risk of mortality. Method : Logistic Regression Decision Tree Clustering KMeans Neural Network Hybrid => Neuro Fuzzy, Fuzzy Subtractive Clustering, etc Comparison of Modelling Technique to Predict Clinical Outcome using Routinely Collected Data

2. Model was built from BHOM dataset, during 12-month study period => 17,417 patients, quarters 1, 2, 3 and 4 (q1,q2,q3,q4). q1 as data training (n 1 =2257), q2, q3, and q4 are data testing (n 2 =2335, n 3 =2361, n 4 =2544) The fields are : death - at discharge - F=alive, T =dead (class attribute) age at admission mode of admission (mostly emergency, but some elective) gender haemoglobin white cell count urea serum sodium serum potassium creatinine urea / creatinine

3. Q2 Q3 Q4

4. Q2Q3 Q4

5. Q2Q3 Q4

6. Logistic regression is an acceptable method by assessing model performance using techniques designed to test both calibration and discrimination This involved use of the χ 2 test to compare frequency tables using the c-index (equivalent to the area under the ROC curve). Investigating methods at Model Assessment for Inducer Classifier and KMeans Clustering. Investigating How to Compare the Different Methods