Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Arun Dev, R.; George, Jossy; Alapatt, Bosco Paul; Baby, Riya Title A name given to the resource Enhancing Diagnostic Accuracy in Familial Alzheimers Disease Through Gene Expression Profiling and Optimized Machine Learning Algorithms Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1355 LNNS;pp.367-376 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-4883-2_31" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-4883-2_31</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010579178?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010579178?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource Alzheimers disease diagnosis; Familial Alzheimers Disease (FAD); Gene expression analysis; Hyperparameter tuning; LightGBM; Linear regression; Machine learning; Predictive modeling; Ridge regression Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981964882-5; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online