Data Mining: Applying and comparing the performance of classification algorithms on a clinical dataset
Our project had 5 main achievements: 1. Applying different well-known prediction algorithms on a clinical dataset obtained from Razan Center for Infertility which are: Naïve Bayes Classifier Random Forest Logistic Regression K-NN algorithm 2. Applying the RIMARC algorithm, which is a supervised (prediction) algorithm that learns a scoring function to rank instances, developed and published as a scientific research and an executable jar file. 3. Compare the performance of RIMARC and the other 4 prediction algorithm regarding the same clinical data using 2 main indicators: Area Under the Roc Curve indicator (AUC). Execution time indicator. 4. Document the results of the previous mentioned experiment in a scientific research under the title of: Applying Prediction Algorithms on ICSI Treatment Related Data for Performance Comparison. 5. Develop a code from the logic of SERA, which is based on the ranking algorithm RIMARC, to estimate the success rate for the couples going through ICSI treatment to help both Razan Center for Infertility and patients with more informed decisions on whether to go through with the treatment or not.