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Cited by 393Dementia and Cognitive Impairment ResearchArtificial Intelligence in HealthcareAlzheimer's disease research and treatments

The DOI matches its Crossref record; 30 of the 31 references it cites were found in a registry or an index, and 1 are not checked yet.

Early-Stage Alzheimer’s Disease Prediction Using Machine Learning Models

Open access
Kavitha, C., Mani, Vinodhini, Srividhya, S. R., Khalaf, Osamah Ibrahim, Tavera Romero, Carlos Andrés·2022·Frontiers in Public Health
DOI 10.3389/fpubh.2022.853294PMC PMC8927715
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Abstract

Alzheimer's disease (AD) is the leading cause of dementia in older adults. There is currently a lot of interest in applying machine learning to find out metabolic diseases like Alzheimer's and Diabetes that affect a large population of people around the world. Their incidence rates are increasing at an alarming rate every year. In Alzheimer's disease, the brain is affected by neurodegenerative changes. As our aging population increases, more and more individuals, their families, and healthcare will experience diseases that affect memory and functioning. These effects will be profound on the social, financial, and economic fronts. In its early stages, Alzheimer's disease is hard to predict. A treatment given at an early stage of AD is more effective, and it causes fewer minor damage than a treatment done at a later stage. Several techniques such as Decision Tree, Random Forest, Support Vector Machine, Gradient Boosting, and Voting classifiers have been employed to identify the best parameters for Alzheimer's disease prediction. Predictions of Alzheimer's disease are based on Open Access Series of Imaging Studies (OASIS) data, and performance is measured with parameters like Precision, Recall, Accuracy, and F1-score for ML models. The proposed classification scheme can be used by clinicians to make diagnoses of these diseases. It is highly beneficial to lower annual mortality rates of Alzheimer's disease in early diagnosis with these ML algorithms. The proposed work shows better results with the best validation average accuracy of 83% on the test data of AD. This test accuracy score is significantly higher in comparison with existing works.

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Registry

Crossref · 10.3389/fpubh.2022.853294

Cites

30 of 31 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Proposed workflow.
Figure 1 Proposed workflow.CC BY · PubMed Central
Figure 2 Representation of data splitting.
Figure 2 Representation of data splitting.CC BY · PubMed Central
Figure 3 Analysis of demented and non-demented rate based on gender, Gender group Female = 0, Male = 1.
Figure 3 Analysis of demented and non-demented rate based on gender, Gender group Female = 0, Male = 1.CC BY · PubMed Central
Figure 4 Analysis of MMSE scores for demented and non-demented group of patients.
Figure 4 Analysis of MMSE scores for demented and non-demented group of patients.CC BY · PubMed Central
Figure 5 (A–C) Analysis of ASF, eTIV and nWBV for Demented and Non-demented group.
Figure 5 (A–C) Analysis of ASF, eTIV and nWBV for Demented and Non-demented group.CC BY · PubMed Central
Figure 6 Analysis on years of education.
Figure 6 Analysis on years of education.CC BY · PubMed Central
Figure 7 Analysis on people affected by demented and non-demented group based on age.
Figure 7 Analysis on people affected by demented and non-demented group based on age.CC BY · PubMed Central
Figure 8 Confusion matrix for decision tree.
Figure 8 Confusion matrix for decision tree.CC BY · PubMed Central
Figure 9 Confusion matrix for random forest.
Figure 9 Confusion matrix for random forest.CC BY · PubMed Central
Figure 10 Confusion matrix for SVM.
Figure 10 Confusion matrix for SVM.CC BY · PubMed Central
Figure 11 Confusion matrix for XGBoost.
Figure 11 Confusion matrix for XGBoost.CC BY · PubMed Central
Figure 12 Confusion matrix for soft voting classifier.
Figure 12 Confusion matrix for soft voting classifier.CC BY · PubMed Central
Figure 13 Confusion matrix for hard voting classifier.
Figure 13 Confusion matrix for hard voting classifier.CC BY · PubMed Central
Figure 14 Comparison of accuracy.
Figure 14 Comparison of accuracy.CC BY · PubMed Central
Figure 15 Comparison of precision.
Figure 15 Comparison of precision.CC BY · PubMed Central
Figure 16 Comparison of recall.
Figure 16 Comparison of recall.CC BY · PubMed Central
Figure 17 Comparison of F1 score.
Figure 17 Comparison of F1 score.CC BY · PubMed Central

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Citations

Cites

1 retracted

Show all 30 references

Research works printed but not linked: 7

  1. Sulaiman, 2014, “Effect of Using Different Propagations of OLSR and DSDV Routing Protocols”, Proceedings of the IEEE International Conference on Intelligent Systems Structureing and Simulation, 540
  2. Osamh Khalaf, 2020, Energy efficient routing and reliable data transmission protocol in WSN, Int J Adv Soft Comput Applicat., 12, 45
  3. 2018, Preventing cognitive decline and dementia: A way forward
  4. Khalaf, 2018, A modified algorithm for improving lifetime WSN, J Eng Appl Sci., 13, 9277
  5. Chyzhyk, 2010, Feature extraction from structural MRI images based on VBM: data from OASIS database, University of The Basque Country, Internal research publication
  6. Sudharsan, 2021, Alzheimer's disease prediction using machine learning techniques and principal component analysis (PCA), Materials Today: Proceedings
  7. Abdulsahib, 2018, An improved algorithm to fire detection in forest by using wireless sensor networks, Int J Civil Eng Technol (IJCIET)., 9, 369

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Metadata

Authors
Kavitha, C., Mani, V., Srividhya, S. R., Khalaf, O. I., & Tavera Romero, C. A.
Affiliations
  • Sathyabama Institute of Science and Technology (IN)
  • Nahrain University (IQ)
Corresponding author
Kavitha, C.
ORCID
Venue
Frontiers in Public Health
Publisher
Frontiers Media SA
Published
2022-03-03
Volume
10
Pages
853294-853294
ISSN
2296-2565 (ISSN-L)
DOI
10.3389/fpubh.2022.853294
PMC
PMC8927715
OpenAlex
W4214898695
Cited by
373
References
30
Citations by year
2026: 64 · 2025: 135 · 2024: 90 · 2023: 56 · 2022: 28
FWCI
50.29
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Public Health (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Universidad Santiago de Cali (01-2021)
  • National Institute on Aging (P01AG003991)
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
Keywords
Alzheimer's disease, dementia, OASIS dataset, neuroimaging, machine learning classification, random forest, gradient boosting, support vector machine, decision tree, voting classifier, early diagnosis
MeSH terms
Machine Learning, Aged, Alzheimer Disease, Brain, Humans, Magnetic Resonance Imaging, Cognitive Dysfunction

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