Year: 2025
Type: conference-paper
Abstract: Context: Alzheimer’s Disease (AD) is a complex neurodegenerative disorder that requires early diagnosis to improve patient outcomes. Recent advances in computational intelligence have sparked interest in leveraging machine learning to enhance diagnostic accuracy and efficiency. These innovations are crucial for transforming decision-making within Information Systems in clinical settings. Problem: Traditional methods like PET-scans and cerebrospinal fluid collection are highly accurate but costly and invasive, limiting accessibility. Developing data-driven, non-invasive solutions that retain diagnostic accuracy while handling complex biomedical data, such as plasma protein concentrations, remains a challenge. Solution: This study utilizes neural networks, specifically Multi-Layer Perceptron (MLP) and One-Dimensional Convolutional Neural Network (1D CNN). Preprocessing included Recursive Feature Elimination (RFE) for feature selection and Synthetic Minority Oversampling Technique (SMOTE) for data augmentation, addressing class imbalance. SI Theory: Grounded in Complexity Theory, the study examines how machine learning models can enhance data-driven medical systems by efficiently managing critical, highly sensitive datasets. Method: An experimental quantitative approach was used to evaluate binary and multiclass classifiers on a dataset with 120 protein features from 259 patients. Summary of Results: The MLP exhibited strong performance in specific subsets, achieving superior metrics in the binary classification after feature selection and data augmentation. Meanwhile, the 1D CNN excelled in multiclass classification, leveraging its convolutional layers to extract critical features from subtle protein variations, improving accuracy and robustness. Contributions and Impact on IS Field: This research enhances medical information systems by proposing machine learning models that can be integrated for accurate diagnostics, supporting clinical decision-making and advancing healthcare practices.
Institution: Universidade Federal de Pernambuco
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
OpenAlex ID: W4410160092