TY - GEN
T1 - Alzheimer Disease Prediction
T2 - 1st EAI International Conference on Next Generation Computing and Communication Applications, ICNGCCA 2025
AU - Ansah, Kwabena
AU - Twumasi, Ampofo
AU - Mensah, Joseph Agyapong
AU - Appati, Justice Kwame
N1 - Publisher Copyright:
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2026.
PY - 2026
Y1 - 2026
N2 - Research indicates that mild cognitive impairment (MCI) progresses to Alzheimer’s disease at an estimated rate of 10–15% per year. A common symptom associated with this progression is memory loss. Traditionally, studies in this field have relied on a single type of data to predict the disease's occurrence. However, recent studies have shown pro4mising results when multiple data modalities are considered. Methodologically, standard convolutional neural networks (CNNs) are typically used in successful deep learning techniques. However, they often face challenges such as large parameter sizes, resulting in computationally expensive operations. In this study, we enhance the prediction performance of Alzheimer’s disease by augmenting patient electronic health records (EHR) with magnetic resonance images (MRI), focusing on metrics like accuracy, precision, and recall. To achieve this, we utilized a stacked denoising autoencoder to generate intermediate relevant features from the patient’s EHR. Concurrently, a depthwise separable convolutional network was employed to extract features from the patient’s MRI data. Experimentally, this multimodal approach demonstrated significant improvements, recording an accuracy of 95.74%, a recall of 95.21%, and a precision of 95.00%, compared to classical CNNs used in existing networks.
AB - Research indicates that mild cognitive impairment (MCI) progresses to Alzheimer’s disease at an estimated rate of 10–15% per year. A common symptom associated with this progression is memory loss. Traditionally, studies in this field have relied on a single type of data to predict the disease's occurrence. However, recent studies have shown pro4mising results when multiple data modalities are considered. Methodologically, standard convolutional neural networks (CNNs) are typically used in successful deep learning techniques. However, they often face challenges such as large parameter sizes, resulting in computationally expensive operations. In this study, we enhance the prediction performance of Alzheimer’s disease by augmenting patient electronic health records (EHR) with magnetic resonance images (MRI), focusing on metrics like accuracy, precision, and recall. To achieve this, we utilized a stacked denoising autoencoder to generate intermediate relevant features from the patient’s EHR. Concurrently, a depthwise separable convolutional network was employed to extract features from the patient’s MRI data. Experimentally, this multimodal approach demonstrated significant improvements, recording an accuracy of 95.74%, a recall of 95.21%, and a precision of 95.00%, compared to classical CNNs used in existing networks.
KW - Alzheimer's Disease
KW - Convolutional Neural Network
KW - Depthwise Separable Convolutional Network
KW - Electronic Health Record
KW - Magnetic Resonance Imaging
KW - Mild Cognitive Impairment
UR - https://www.scopus.com/pages/publications/105028088201
U2 - 10.1007/978-3-032-13009-9_7
DO - 10.1007/978-3-032-13009-9_7
M3 - Conference contribution
AN - SCOPUS:105028088201
SN - 9783032130082
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 75
EP - 88
BT - Next Generation Computing and Communication Applications - First EAI International Conference, ICNGCCA 2025, Proceedings
A2 - Kumar, Raghvendra
A2 - Parida, Priyadarsan
A2 - Pattnaik, Prasant Kumar
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 18 March 2025 through 18 March 2025
ER -