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首页> 外文会议>IEEE International Conference on Electro Information Technology >A Stacking-based Ensemble Learning Model with Genetic Algorithm For detecting Early Stages of Alzheimer’s Disease
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A Stacking-based Ensemble Learning Model with Genetic Algorithm For detecting Early Stages of Alzheimer’s Disease

机译：一种基于堆叠的遗传算法，用于检测阿尔茨海默病的早期阶段

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Alzheimer's disease (AD) affects fifty million people worldwide and is the sixth cause of death in the United States. However, there is no cure or treatment for patients with AD; thus, it is important to detect this disease at an early stage to improve patients' lives qualities. Several studies have been proposed to detect and differentiate between different AD groups, although most of these works only focused on differentiating between healthy people and people with Alzheimer's. These studies also did not identify the most reliable biomarkers to provide more accurate results and did not use the best hyperparameters to provide optimal results. To address these issues, we developed a model that leads to a better performance in differentiating between healthy people (cognitively normal), people with mild cognitive impairment, and people with Alzheimer’s disease. For this purpose, we combined a stacking-based ensemble learning, consisting of four traditional classifiers, with a hyperparameter tuning technique, a genetic algorithm. The model was evaluated in terms of accuracy, precision, recall, and F1-score. The simulation results show that stacking-based ensemble learning, using genetic algorithm, provides 96.7% accuracy, 96.5% recall, 97.9% precision, and 97.1% F1-score in differentiating between CN, MCI, and AD groups.
机译：阿尔茨海默病（AD）影响全球五百万人，是美国死亡的第六次。然而，AD患者没有治愈或治疗;因此，在早期阶段检测这种疾病是重要的，以改善患者的生命质量。已经提出了几项研究来检测和区分不同的广告组，尽管这些作品中的大多数仅关注与阿尔茨海默氏症的健康人和人之间的区别。这些研究也没有确定最可靠的生物标志物，以提供更准确的结果，并且没有使用最佳的超参数来提供最佳结果。为了解决这些问题，我们开发了一个模型，导致更好的性能，以区分健康人（认知正常），人们轻度认知障碍，以及阿尔茨海默病的人们。为此目的，我们将由四个传统分类器组成的基于堆叠的集合学习组成，具有良次分类技术，一种遗传算法。该模型是在准确性，精度，召回和F1分数方面进行评估的。仿真结果表明，基于堆叠的集合学习，使用遗传算法提供96.7％的精度，96.5％的召回，97.9％精度，97.1％的F1分数在CN，MCI和广告组之间区分。

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