## 1 Introduction  
Alzheimer's disease (AD) is a dysfunctional brain disorder that gradually affects the patient's cognitive abilities. It is the most common type of dementia and typically manifests as memory impairment. Memory disorder typically develops slowly and progressively over time. At first, memory impairment is limited to recent events, but long-term memories are gradually damaged over time. Many people around the world are affected by AD [1]. By 2050, it is projected that 12.7 million Americans will have AD, an increase from the current estimate prevalency of 6.5 million [2]. Generally, AD is classified into two main phases: mild cognitive impairment (MCI) and dementia due to AD. The preclinical phase starts years before the disease is identified [3]. MCI is the stage that occurs between cognitively health and dementia due to AD [4]. Patients with MCI experience more cognitive deterioration than would be expected with normal aging, yet there are no obvious disruptions to their daily activities [5]. Dementia due to AD is the final phase which is associated with striking behavioral and cognitive abnormalities. [6]. It is documented that 10-15% of MCI subjects are at risk of developing dementia due to AD, which is known as progressive-MCI (pMCI), while the rest remain stable (stable-MCI or sMCI) or return to a cognitively healthy condition [7, 8]. Clinically, MCI conversion prediction is crucial as it would help doctors to identify MCI patients who are at risk of developing dementia due to AD. Identifying pMCI patients at this stage can lead to early interventions and better management of the condition, including regular monitoring of cognitive function and appropriate support for patients. The accurate prediction of MCI conversion is also important for clinical trials and research studies aimed at developing new treatments for AD as it helps ensure that clinical trials are conducted on the right patient population, increasing the likelihood of success. For example, the U.S. Food and Drug Administration (FDA) recently confirmed Aducanumab (Aduhelm) as a treatment for AD. This medicine, which diminishes amyloid plaques in the brain, can be used to treat AD patients in the MCI or mild dementia stages. However, Aduhelm may not be appropriate for all MCI patients and it is recommended only for MCI patients who are at risk of developing dementia due to AD (i.e., pMCI patients). Thus, many researchers have concentrated on developing innovative and robust algorithms for identifying MCI patients who are at risk of developing dementia [9; 10; 11]. In the last two decades, neuroimaging techniques such as structural magnetic resonance imaging (sMRI), functional magnetic resonance imaging (fMRI), and positron emission tomography (PET) have been extensively used for AD analysis [12]. Among these imaging techniques, sMRI was widely employed to diagnose AD as this modality is able to provide a high-resolution image of the brain that contains vital information and details about brain tissues. In addition, unlike PET or single photon emission computed tomography (SPECT), sMRI does not require a radioactive pharmaceutical injection. In this study, we concentrated on sMRI data at the baseline for MCI conversion prediction. In addition to neuroimaging data, machine learning (ML) models have been used for early AD or monitoring AD progression [13]. An MCI conversion prediction framework using ML methods typically consists of the following steps: preprocessing, feature extraction, feature selection, and classification. Preprocessing techniques are mainly used to enhance brain images through noise removal and the elimination of unrelated parts. The aim of the feature extraction stage is to extract informative features from preprocessed brain scans for AD detection.