Machine learning and artificial neural network for data mining classification and prediction of brain diseases

Recently artificial intelligence (AI), machine learning (ML) and deep learning (DL) got the most of researchers' attention in different aspects of computing applications and areas such as classification, prediction, etc. However, the development of data mining and its availability assists in the performance evaluation of such models. In this research, different intelligent algorithms (XGBoost, decision tree (DT), random forest, K-NN, ANN, LDA and AdaBoost) were implemented and tested for evaluation and performance. It is worth mentioning that ANN is a DL algorithm while all other algorithms lie in the field of ML. These models were implemented on a combination of Kaggle stroke and Parkinson brain diseases dataset. The performance evaluation of these algorithms computed according to different metrics including precision, recall, f1-score, AUC and accuracy. The accuracy of these models was 97.04% for XGBoost, 95.2% for DT, 97.06% for random forest, 95.02% for K-NN, 95.03% for SVM, 94.95% for logistic regression, 93% for ANN, 94.23% for LDA and 94.71% for AdaBoost. The highest AUC performance was 93.35% for logistic regression. Finally, a comparison in performance with other research was evaluated in terms of accuracy. © 2023 Inderscience Publishers. All rights reserved.