Abstract
Dementia being a major cause of creating dependency among aged people also has an inevitable impact on people suffering from it and the families around them. Since the symptoms are gradual and may overlap, diagnosing dementia and identifying its type is risky. The main purpose is to develop a machine learning-based method in diagnosing dementia using the dataset obtained from OASIS. Algorithms such as Support Vector Machine, AdaBoost, K-Nearest Neighbors, Random forest, Linear Discriminant Analysis, XgBoost algorithms are used to find accuracy, recall, and confusion matrix. Implementation of the following algorithms provides accuracy in the range of 83 to 90 percent. SVM provides 87%, KNN gives out accuracy of 84%, LDA gives an accuracy of 83%, Random forest gives an accuracy of 88%, AdaBoost gives 81% and XgBoost gives 90%. XGBoost shows more accuracy than other algorithms.
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This article (and all articles in the proceedings volume relating to the same conference) has been retracted by IOP Publishing following an extensive investigation in line with the COPE guidelines. This investigation has uncovered evidence of systematic manipulation of the publication process and considerable citation manipulation.
IOP Publishing respectfully requests that readers consider all work within this volume potentially unreliable, as the volume has not been through a credible peer review process.
IOP Publishing regrets that our usual quality checks did not identify these issues before publication, and have since put additional measures in place to try to prevent these issues from reoccurring. IOP Publishing wishes to credit anonymous whistleblowers and the Problematic Paper Screener [1] for bringing some of the above issues to our attention, prompting us to investigate further.
[1] Cabanac G, Labbé C and Magazinov A 2021 arXiv:2107.06751v1
Retraction published: 23 February 2022