Author:
Parvathavarthini S.,Pranethaa S. V.,Santhiya P.,Sanuja S.
Publisher
Springer Nature Singapore
Reference15 articles.
1. Chieh, J.J., Chang, L.M., Chiu, Y.N., Chien, Y.H., Huang, P.T., Huang, A.C., Hwu, W.L., Lee, N.C., Yang, S.Y.: Blood beta-amyloid and tau in down syndrome: a comparison with Alzheimer’s disease. Front Aging Neurosci. 8, 316 (2017). https://doi.org/10.3389/fnagi.2016.00316
2. Breitner, J., Labonté, A., Meyer, P.F., Rosa-Neto, P., Town, T., Savard, M., Poirier, J., Weitz, T.M., Town, T., Alzheimer’s Disease Neuroimaging Initiative; PREVENT-AD Research Group: Bi-directional association of cerebrospinal fluid ımmune markers with stage of Alzheimer’s Disease pathogenesis. J. Alzheimers Dis. 63(2), 577–590 (2018). https://doi.org/10.3233/JAD-170887
3. Antor, M.B., Jamil, A.S., Mamtaz, M., Khan, M.M., Aljahdali, S., Kaur, M., Singh, P., Masud, M.: A comparative analysis of machine learning algorithms to predict Alzheimer’s Disease. J. Healthc. Eng. 2021, 9917919 (2021)
4. Alroobaea, R., Mechti, S., Haoues, M., et al.: Alzheimer’s Disease early detection using machine learning techniques. Preprint (Version 1) available at Research Square (17 June 2021).https://doi.org/10.21203/rs.3.rs-624520/v1
5. Grueso, S., Viejo-Sobera, R.: Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer’s disease dementia: a systematic review. Alz. Res. Ther. 13(1), 162 (2021). https://doi.org/10.1186/s13195-021-00900-w