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A cohort of 487 enrolled participants was categorized into three groups: normal control (138 individuals), mild cognitive impairment (238 patients), and AD (111 patients) in this study. An improved multifeature squeeze-and-excitation-dilated residual network (MFSE-DRN) was proposed for two important AD predictions: clinical scores and conversion probability. The model was characterized as three modules: squeeze-and-excitation-dilated residual block (SE-DRB), multifusion pooling (MF-Pool), and multimodal feature fusion. To assess its performance, the proposed model was compared with two other novel models: ranking convolutional neural network (RCNN) and 3D vision geometrical group network (3D-VGGNet).\u00a0Our method showed the best performance in the two AD predicted tasks. For the clinical scores prediction, the root-mean-square errors (RMSEs) and mean absolute errors (MAEs) of mini-mental state examination (MMSE) and AD assessment scale\u2013cognitive 11-item (ADAS-11) were 1.97, 1.46 and 4.20, 3.19 within 6\u00a0months; 2.48, 1.69 and 4.81, 3.44 within 12\u00a0months; 2.67, 1.86 and 5.81, 3.83 within 24\u00a0months; 3.02, 2.03 and 5.09, 3.43 within 36\u00a0months, respectively. At the AD conversion probability prediction, the prediction accuracies within 12, 24, and 36\u00a0months reached to 88.0, 85.5, and 88.4%, respectively. The AD predication would play a great role in clinical applications.<\/jats:p>","DOI":"10.1038\/s41598-024-62712-w","type":"journal-article","created":{"date-parts":[[2024,5,25]],"date-time":"2024-05-25T07:01:39Z","timestamp":1716620499000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Intelligent prediction of Alzheimer\u2019s disease via improved multifeature squeeze-and-excitation-dilated residual network"],"prefix":"10.1038","volume":"14","author":[{"given":"Zengbei","family":"Yuan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinlin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zezhou","family":"Hao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhixian","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xufeng","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,5,25]]},"reference":[{"key":"62712_CR1","doi-asserted-by":"publisher","first-page":"107371","DOI":"10.1016\/j.asoc.2021.107371","volume":"106","author":"R Sharma","year":"2021","unstructured":"Sharma, R., Goel, T., Tanveer, M., Dwivedi, S. & Murugan, R. 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