Skip to main content
Springer Nature Link
Log in

Early diagnosis model of Alzheimer’s Disease based on sparse logistic regression

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Accurate classification of Alzheimer’s Disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI) are critical for the effective treatment of AD. However, compared with AD classification tasks, predicting the conversion of MCI to AD is relatively difficult. as there are only minor differences among MCI groups. What’s more, in brain imaging analysis, the high dimensionality and relatively small number of subjects brings challenges to computer-aided diagnosis of AD and MCI. Many previous researches focused on the identification of imaging biomarkers for AD diagnosis. In this paper, we introduce sparse logistic regression for the early diagnosis of AD. Sparse logistic regression (SLR) uses L1/2 regularization to impose a sparsity constraint on logistic regression. The L1/2 regularization is considered a representative of Lq regularization, where fewer but informative key brain regions are applied for the classification of AD/MCI. We evaluated the SLR on 197 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results showed that the SLR improves the classification performance of AD/MCI compared other classical methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Acharjya D (2020) A hybrid scheme for heart disease diagnosis using rough set and cuckoo search technique. J Med Syst 44(1):27

    Google Scholar 

  2. Ahmed OB, Benois-Pineau J, Allard M, Catheline G, Amar CB, Initiative AsDN (2017) Recognition of Alzheimer's disease and mild cognitive impairment with multimodal image-derived biomarkers and multiple kernel learning. Neurocomputing 220:98–110

    Google Scholar 

  3. Algamal ZY, Lee MH (2015) Penalized logistic regression with the adaptive LASSO for gene selection in high-dimensional cancer classification. Expert Syst Appl 42(23):9326–9332

    Google Scholar 

  4. Breheny P, Huang J (2011) Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection. Ann Appl Stat 5(1):232–253

    MathSciNet  MATH  Google Scholar 

  5. Cai J, Hu L, Liu Z, Zhou K, Zhang H (2020) An embedded feature selection and multi-class classification method for detection of the progression from mild cognitive impairment to Alzheimer’s disease. J Med Imaging Health Inform 10(2):370–379

    Google Scholar 

  6. Cheng B, Liu M, Shen D, Li Z, Zhang D, Initiative AsDN (2017) Multi-domain transfer learning for early diagnosis of Alzheimer’s disease. Neuroinformatics 15(2):115–132

    Google Scholar 

  7. Chowdhary CL, Acharjya D (2016) A hybrid scheme for breast cancer detection using intuitionistic fuzzy rough set technique. Int J Healthcare Info Syst Inform 11(2):38–61

    Google Scholar 

  8. Chowdhary CL, Acharjya D (2017) Clustering algorithm in possibilistic exponential fuzzy c-mean segmenting medical images. J Biomimet Biomater Biomed Eng 30:12–23

    Google Scholar 

  9. Chowdhary CL, Acharjya D (2018) Segmentation of mammograms using a novel intuitionistic possibilistic fuzzy c-mean clustering algorithm. Nature Inspired Computing 75-82

  10. Chowdhary CL, Acharjya D (2020) Segmentation and feature extraction in medical imaging: a systematic review. Procedia Comp Sci 167:26–36

    Google Scholar 

  11. Chowdhary CL, Sai GVK, Acharjya D (2016) Decrease in false assumption for detection using digital mammography. Comput Intell Data Min 2:325–333

    Google Scholar 

  12. Cui R, Liu M, Imaging AsDNIJCM, Graphics (2019) RNN-based longitudinal analysis for diagnosis of Alzheimer’s disease. Comput Med Imaging Graph 73:1–10

    Google Scholar 

  13. Dai W, Lopez OL, Carmichael OT, Becker JT, Kuller LH, Gach HM (2009) Mild cognitive impairment and alzheimer disease: patterns of altered cerebral blood flow at MR imaging. Radiology 250(3):856–866

    Google Scholar 

  14. Del Sole A, Clerici F, Chiti A, Lecchi M, Mariani C, Maggiore L, Mosconi L, Lucignani G (2008) Individual cerebral metabolic deficits in Alzheimer’s disease and amnestic mild cognitive impairment: an FDG PET study. Eur J Nucl Med Mol Imaging 35(7):1357–1366

    Google Scholar 

  15. Eshkoor SA, Hamid TA, Mun CY, Ng CK (2015) Mild cognitive impairment and its management in older people. Clin Interv Aging 10:687

    Google Scholar 

  16. Friedman J, Hastie T, Tibshirani R (2010) Regularization paths for generalized linear models via coordinate descent. J Stat Softw 33(1):1

    Google Scholar 

  17. Gómez-Sancho M, Tohka J, Gómez-Verdejo V, Initiative AsDN (2018) Comparison of feature representations in MRI-based MCI-to-AD conversion prediction. Magn Reson Imaging 50:84–95

    Google Scholar 

  18. Karas G, Scheltens P, Rombouts S, Van Schijndel R, Klein M, Jones B, Van Der Flier W, Vrenken H, Barkhof FJN (2007) Precuneus atrophy in early-onset Alzheimer’s disease: a morphometric structural MRI study. Neuroradiology 49(12):967–976

    Google Scholar 

  19. Koh K, Kim S-J, Boyd S (2007) An interior-point method for large-scale l1-regularized logistic regression. J Mach Learn Res 8:1519–1555

    MathSciNet  MATH  Google Scholar 

  20. Li F, Tran L, Thung K-H, Ji S, Shen D, Li J (2015) A robust deep model for improved classification of AD/MCI patients. IEEE J Biomed health Inform 19(5):1610–1616

    Google Scholar 

  21. Liang Y, Liu C, Luan X-Z, Leung K-S, Chan T-M, Xu Z-B, Zhang H (2013) Sparse logistic regression with a L 1/2 penalty for gene selection in cancer classification. BMC Bioinform 14(1):198

    Google Scholar 

  22. Liu F, Wee C-Y, Chen H, Shen D (2014) Inter-modality relationship constrained multi-modality multi-task feature selection for Alzheimer's disease and mild cognitive impairment identification. NeuroImage 84:466–475

    Google Scholar 

  23. Liu F, Huang X, Gong C, Yang J, Suykens JA (2018) Indefinite kernel logistic regression with concave-inexact-convex procedure. IEEE Transact Neur Netw Learn Syst 30(3):765–776

    MathSciNet  Google Scholar 

  24. Liu M, Li F, Yan H, Wang K, Ma Y, Shen L, Xu M, Initiative AsDN (2020) A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer’s disease. NeuroImage 208:116459

    Google Scholar 

  25. Matsuda H (2013) Voxel-based morphometry of brain MRI in normal aging and Alzheimer’s disease. Aging Dis 4(1):29–37

    Google Scholar 

  26. Meinshausen N, Yu B (2009) Lasso-type recovery of sparse representations for high-dimensional data. Ann Stat 37(1):246–270

    MathSciNet  MATH  Google Scholar 

  27. Min R, Wu G, Cheng J, Wang Q, Shen D, Initiative AsDN (2014) Multi-atlas based representations for Alzheimer's disease diagnosis. Hum Brain Mapp 35(10):5052–5070

    Google Scholar 

  28. Moradi E, Pepe A, Gaser C, Huttunen H, Tohka J, Initiative AsDN (2015) Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects. Neuroimage 104:398–412

    Google Scholar 

  29. Papakostas GA, Savio A, Graña M, Kaburlasos VG (2015) A lattice computing approach to Alzheimer’s disease computer assisted diagnosis based on MRI data. Neurocomputing 150:37–42

    Google Scholar 

  30. Patterson C (2018) The state of the art of dementia research: new frontiers. World Alzheimer Report

  31. Petersen RC (2016) Mild cognitive impairment. CONTINUUM: Lifelong Learn Neurol 22(2):404

    Google Scholar 

  32. Qiu Z, Miller DJ, Kesidis G (2016) A maximum entropy framework for semisupervised and active learning with unknown and label-scarce classes. IEEE Transact Neur Netw Learn Syst 28(4):917–933

    Google Scholar 

  33. Ruiz E, Ramírez J, Górriz JM, Casillas J, Initiative AsDN (2018) Alzheimer’s disease computer-aided diagnosis: histogram-based analysis of regional MRI volumes for feature selection and classification. J Alzheimers Dis 65(3):819–842

    Google Scholar 

  34. Tripathy B, Acharjya D, Cynthya V (2013) A framework for intelligent medical diagnosis using rough set with formal concept analysis

  35. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, Joliot M (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15(1):273–289

    Google Scholar 

  36. Vandewater L, Brusic V, Wilson W, Macaulay L, Zhang P (2015) An adaptive genetic algorithm for selection of blood-based biomarkers for prediction of Alzheimer's disease progression. BMC Bioinform 16(18):S1

    Google Scholar 

  37. Wang Y, Liu W, Caccetta L, Zhou G (2015) Parameter selection for nonnegative l1 matrix/tensor sparse decomposition. Oper Res Lett 43(4):423–426

    MathSciNet  MATH  Google Scholar 

  38. Wang Q, Zheng Y, Yang G, Jin W, Chen X, Yin Y (2017) Multiscale rotation-invariant convolutional neural networks for lung texture classification. IEEE J Biomed health Inform 22(1):184–195

    Google Scholar 

  39. Wang R, Xiu N, Zhang C (2019) Greedy projected gradient-Newton method for sparse logistic regression. IEEE Transact Neur Netw Learn Syst 31(2):527–538

    MathSciNet  Google Scholar 

  40. Wee C-Y, Yap P-T, Zhang D, Denny K, Browndyke JN, Potter GG, Welsh-Bohmer KA, Wang L, Shen DJN (2012) Identification of MCI individuals using structural and functional connectivity networks. Neuroimge 59(3):2045–2056

    Google Scholar 

  41. Wee CY, Yap PT, Shen D, Initiative AsDN (2013) Prediction of Alzheimer's disease and mild cognitive impairment using cortical morphological patterns. Hum Brain Mapp 34(12):3411–3425

    Google Scholar 

  42. Xu Z, Zhang H, Wang Y, Chang X, Liang Y (2010) L 1/2 regularization. SCIENCE CHINA Inf Sci 53(6):1159–1169

    MathSciNet  Google Scholar 

  43. Xu Z, Zhang H, Wang Y, Chang X, Liang YJSCIS (2010) L 1/2 regularization. SCIENCE CHINA Inf Sci 53(6):1159–1169

    MathSciNet  Google Scholar 

  44. Ye T, Zu C, Jie B, Shen D, Zhang D, Initiative AsDN (2016) Discriminative multi-task feature selection for multi-modality classification of Alzheimer’s disease. Brain Imaging Behav 10(3):739–749

    Google Scholar 

  45. Yu G, Liu Y, Shen D (2016) Graph-guided joint prediction of class label and clinical scores for the Alzheimer’s disease. Brain Struct Funct 221(7):3787–3801

    Google Scholar 

  46. Zakariya Yahya A, Muhammad Hisyam L (2015) Regularized logistic regression with adjusted adaptive elastic net for gene selection in high dimensional cancer classification. Comput Biol Med 67:136–145

    Google Scholar 

  47. Zhang D, Wang Y, Zhou L, Yuan H, Shen D, Initiative AsDN (2011) Multimodal classification of Alzheimer's disease and mild cognitive impairment. Neuroimage 55(3):856–867

    Google Scholar 

  48. Zhang X, Hu B, Ma X, Xu L (2015) Resting-state whole-brain functional connectivity networks for mci classification using l2-regularized logistic regression. IEEE Transact Nanobiosci 14(2):237–247

    Google Scholar 

Download references

Acknowledgments

This work is partially supported by National Nature Science Foundation of China (71971190); Ministry of Education Humanities and Social Science Project (11YJCZH021, 15YJCZH111). Shandong Social Science Planning Research Project (17CHLJ41, 16CTQJ02, 18CHLJ34).

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Qufu Normal University, Rizhao, 276800, China

    Ruyi Xiao, Xinchun Cui & Yiquan Zhang

  2. Business School, Shandong Normal University, Jinan, 250014, China

    Hong Qiao

  3. School of Information Science and Engineering, Shandong Normal University, Jinan, 250014, China

    Xiangwei Zheng

Authors
  1. Ruyi Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xinchun Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hong Qiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiangwei Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yiquan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xinchun Cui.

Ethics declarations

Conflict of interest

The authors declare no financial or commercial conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiao, R., Cui, X., Qiao, H. et al. Early diagnosis model of Alzheimer’s Disease based on sparse logistic regression. Multimed Tools Appl 80, 3969–3980 (2021). https://doi.org/10.1007/s11042-020-09738-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09738-0

Keywords