A New Prediction Approach for Preventing Default Customers from Applying Personal Loans Using Machine Learning

In the Egyptian banking industry, loan officers use pure judgment to make personal loan approval decisions. In this paper, we develop a new predictive method for default customers' loans using machine learning. The new predictive method uses the available personal data and historical credit data to evaluate the credit trust-worthiness of customers to obtain loans. We used the ABE dataset for training and testing, as we used 10 features from the application form and i- score report class that could give great help to credit officers for taking the right decision through avoiding customer selection using random techniques. The collected dataset was analysed by using various machine learning classifiers based on important selected features, to obtain high accuracy. We compared the performance of several machine learning classifiers before and after feature selection. We have found that in terms of high accuracy, the most important features are (activity – income – loan) and in terms of better performance the decision tree classifier has surpassed any other machine learning classifier with significant prediction accuracy of almost 94.85%.

A multivariate modeling method for the prediction of low fetal fraction before noninvasive prenatal testing

Objective: To investigate factors associated with fetal fraction and to develop a new predictive method for low fetal fraction before noninvasive prenatal testing. Methods: The study was a retrospective cohort analysis based on the results of noninvasive prenatal testing, complete blood count, thyroxin test, and Down's syndrome screening during the first or second trimester in 14,043 pregnant women. Random forests algorithm was applied to predict the low fetal fraction status (fetal fraction < 4%) through individual information and laboratory records. The performance of the model was evaluated and compared to predictions using maternal weight. Results: Of 14,043 cases, maternal weight, red blood cell, hemoglobin, and free T3 were significantly negatively correlated with fetal fraction while gestation age, free T4, pregnancy-associated plasma protein-A, alpha-fetoprotein, unconjugated estriol, and β-human chorionic gonadotropin were significantly positively correlated with fetal fraction. Compared to predictions using maternal weight as an isolated parameter, the model had a higher area under the curve of receiver operating characteristic and overall accuracy. Conclusions: The comprehensive predictive method based on combined multiple factors was more effective than a single-factor model in low fetal fraction status prediction. This method can provide more pretest quality control for noninvasive prenatal testing.