Innovative Method for Alzheimer Disease Prediction using GP-ELM-RNN
- Title
- Innovative Method for Alzheimer Disease Prediction using GP-ELM-RNN
- Creator
- Abd Algani Y.M.; Vidhya S.; Ghai B.; Acharjee P.B.; Kathiravan M.N.; Dwivedi V.K.
- Description
- Brain illnesses are notoriously challenging because of their fragility, surgical complexity, and high treatment costs. Contrarily, it is not obligatory to carry out the operation, as the outcomes of the procedure may fall short of expectations. Adult-onset Alzheimer's disease, which causes memory loss and losing information to varied degrees, is one of the most common brain diseases. This will vary from person to person based on their current health situation. This highlights the need of using CT brain scans to classify the extent of memory loss and determine the patient's risk for Alzheimer's disease. The four main goals of Alzheimer's disease detection are preprocessing the data, extracting features, selecting features, and training the model with GP-ELM-RNN. The Replicator Neural Network has been utilized earlier for AD detection, however this study offers an improved version of the network, modified with ELM learning and the Garson algorithm. From this study, it is deduced that the proposed method is not only efficient, but also quite precise. In this research, GP-ELM-RNN network is built to four groups of images representing different stages of Alzheimer's disease: very mildly demented, mildly demented, averagely demented, and non-demented. The class of very mildly demented patients was found to have the highest accuracy (99.1%) and specificity (0.984%). As compared to the ELM and RNN models, this technique achieves superior accuracy (around 99.23%). 2023 IEEE.
- Source
- Proceedings of the 2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023, pp. 723-728.
- Date
- 2023-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Alzheimer Disease (AD); GP (Garson-Purned); Grey Level Co-occurrence Matrix (GLCM)
- Coverage
- Abd Algani Y.M., The Arab Academic College for Education in Israel-Haifa, Department of Mathematics, Israel; Vidhya S., Rmk College of Engineering and Technology, S&H(Mathematics), Tamilnadu, Chennai, India; Ghai B., Ccsit Teerthankar Mahaveer University, Uttar Pradesh, Moradabad, India; Acharjee P.B., Christ University, Faculty Member, Maharashtra, Pune, India; Kathiravan M.N., Dr. N.G.P Arts and Science College, Department of Biotechnology, Tamil Nadu, Coimbatore, India; Dwivedi V.K., Vishwavidyalaya Engineering College, Department of Mathematics, Chhattisgarh, Surguja, India
- Rights
- Restricted Access
- Relation
- ISBN: 978-166545630-2
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Abd Algani Y.M.; Vidhya S.; Ghai B.; Acharjee P.B.; Kathiravan M.N.; Dwivedi V.K., “Innovative Method for Alzheimer Disease Prediction using GP-ELM-RNN,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19925.