Alshamlan Hala, Alwassel Arwa, Banafa Atheer, Alsaleem Layan
Department of Information Technology, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia.
Diagnostics (Basel). 2024 Oct 7;14(19):2237. doi: 10.3390/diagnostics14192237.
Machine learning (ML) has increasingly been utilized in healthcare to facilitate disease diagnosis and prediction. This study focuses on predicting Alzheimer's disease (AD) through the development and comparison of ML models using Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR) algorithms. Additionally, feature selection techniques including Minimum Redundancy Maximum Relevance (mRMR) and Mutual Information (MI) were employed to enhance the model performance. The research methodology involved training and testing these models on the OASIS-2 dataset, evaluating their predictive accuracies. Notably, LR combined with mRMR achieved the highest accuracy of 99.08% in predicting AD. These findings underscore the efficacy of ML algorithms in AD prediction and highlight the utility of the feature selection methods in improving the model performance. This study contributes to the ongoing efforts to leverage ML for more accurate disease prognosis and underscores the potential of these techniques in advancing clinical decision-making.
机器学习（ML）在医疗保健领域的应用日益广泛，以促进疾病的诊断和预测。本研究专注于通过使用支持向量机（SVM）、随机森林（RF）和逻辑回归（LR）算法开发和比较ML模型来预测阿尔茨海默病（AD）。此外，还采用了包括最小冗余最大相关性（mRMR）和互信息（MI）在内的特征选择技术来提高模型性能。研究方法包括在OASIS - 2数据集上对这些模型进行训练和测试，评估它们的预测准确性。值得注意的是，LR与mRMR相结合在预测AD时达到了99.08%的最高准确率。这些发现强调了ML算法在AD预测中的有效性，并突出了特征选择方法在提高模型性能方面的效用。本研究为利用ML进行更准确的疾病预后的持续努力做出了贡献，并强调了这些技术在推进临床决策方面的潜力。
