ItemConference Paper

Enhancing Diagnostic Accuracy in Familial Alzheimers Disease Through Gene Expression Profiling and Optimized Machine Learning Algorithms

Title
Enhancing Diagnostic Accuracy in Familial Alzheimers Disease Through Gene Expression Profiling and Optimized Machine Learning Algorithms
Creator
Arun Dev, R.; George, Jossy; Alapatt, Bosco Paul; Baby, Riya
Description
The abstract should summarize the contents of the paper in short terms, i.e. 150250 words Early and accurate diagnosis of the Familial Alzheimers Disease (FAD) is critical for effective treatment of this genetically inherited form of Alzheimers disease. A prediction of FAD from gene expression data is investigated and the performance of various machine learning models on the discovered patterns is evaluated. We compare the output of Linear, Ridge Regression and a LightGBM model with hyper-tuned parameters on data from the Gene Expression Omnibus. The LightGBM model is then hyperparameter tuned to better capture the non-linear complexity of the data. To find the predictive performance, a model is evaluated using MSE, R squared and accuracy. The results show that both the LightGBM model and the traditional models have lower MSE, higher R squared and better accuracy. By examining FAD data on high-dimensional gene expression data these results show that when dealing with high-dimensional gene expression data, sophisticated machine learning models perform better than other approaches, such as LightGBM show higher diagnostic accuracy in FAD. It is shown in this research the power of machine learning is immense and is a powerful tool for the predictive modeling of Alzheimers Disease, as well as possible early detection and personalized treatment. Future work might also aim to further improve model performance with other more complex genetic datasets. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Source
Lecture Notes in Networks and Systems;Volume;1355 LNNS;pp.367-376
Date
01-01-2025
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
Alzheimers disease diagnosis; Familial Alzheimers Disease (FAD); Gene expression analysis; Hyperparameter tuning; LightGBM; Linear regression; Machine learning; Predictive modeling; Ridge regression
Coverage
Arun Dev R., Department of Computer Science, CHRIST University, Bangalore, India; George J., Department of Computer Science, CHRIST University, Bangalore, India; Alapatt B.P., Department of Computer Science, CHRIST University, Bangalore, India; Baby R., Department of Computer Science, CHRIST University, Bangalore, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 23673370; ISBN: 978-981964882-5;
Format
online
Language
English
Type
Conference paper

Citation

Arun Dev, R.; George, Jossy; Alapatt, Bosco Paul; Baby, Riya, “Enhancing Diagnostic Accuracy in Familial Alzheimers Disease Through Gene Expression Profiling and Optimized Machine Learning Algorithms,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25552.

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