## 1 Introduction  
Dementia is one of the leading causes of death worldwide due to population growth and aging [1]. Alzheimer's disease (AD) accounts for the majority of dementia cases and is a progressive neurodegenerative disease that causes memory loss, cognitive impairment, behavioral changes, and death [2]. One of the characteristics of [PERSON]'s dementia is the formation of amyloid plaques due to the deposition of abnormal neuropathological proteins in the early stages [3]. Accordingly, amyloid biomarkers detect the deposition of amyloid plaques in the brain and are used as a major diagnostic tool for the clinical diagnosis and prediction of [PERSON]'s dementia. However, the exact pathogenesis of [PERSON]'s dementia is very complex, and it can develop even without protein deposition. To compensate for this, an 18F-fluorodeoxyglucose (FDG) test, which checks blood flow to the brain, is performed to confirm [PERSON]'s dementia. Unfortunately, performing multiple such tests involves an economic and physical cost to the patient, and undergoing multiple tests causes anxiety in patients and treatment delays [4]. Therefore, in the clinical environment, much effort has been expended to find a way to accurately diagnose [PERSON]'s dementia at an early stage without multiple tests.  
One of the efforts is to utilize the amyloid positron emission tomography (amyloid PET) biomarker as a dual-phase biomarker using it as the early-phase amyloid PET image. Many previous studies have shown that the early-phase amyloid PET image is similar to the FDG biomarker image and can therefore replace it [5; 6; 7]. However, it cannot do so clearly, and considering the noise, it is difficult to analyze for readers with little experience. Inexperienced readers are more vulnerable to subjectivity because they have difficulty making visual assessments that depend heavily on their previous experience, resulting in high inter-rater variability [8].  
Recently, artificial intelligence algorithms have made significant contributions to medical diagnosis. In the past decade, the use of machine learning (ML) algorithms for the classification of Alzheimer's has been very beneficial because it has solved the problem of inter-rater variability and has reduced the physical labor for researchers and clinicians. Previous studies have used various methods, such as support vector machines, naive Bayes, artificial neural networks, and deep learning [9; 10]. Additionally, some researchers have used ensemble strategies to improve the effectiveness of AD diagnosis/ prognosis [11; 12; 13]. These studies frequently proposed predictive models utilizing unimodal or multimodal data, such as magnetic resonance imaging, computed tomography, or positron emission tomography. This approach using multiple types of information could help solve the problem of understanding the heterogeneous physiological characteristics of AD.  
Referring to previous studies showing that early amyloid PET can be used instead of FDG, we performed a comparative analysis to confirm that using dual-phase \({}^{18}\)F-florbetaben (dual FBB) has better classification performance than using delay-phase FBB (dFBB) with ML. This study makes the following contributions. First, this study confirms that the classification performance with dual FBB is better than that with dFBB using ML algorithms. Second, using feature selection with ML, this study confirmed the effect of each brain region corresponding to the composite standardized uptake value ratio (SUVR) of FBB on diagnosis classification.