Risk Prediction Model for Dementia by Deep Learning Using Clinical Data

Year 2022, Volume: 7 Issue: 2, 21 - 24, 31.12.2022

Onural Özhan , Zeynep Küçükakçalı , İpek Balıkçı Çiçek

https://doi.org/10.52876/jcs.1188283

Abstract

It is estimated that dementia, which is the most important public health problem in the elderly, will increase day by day. It is stated that this situation will create great challenges for public health and aged care systems in all countries of the world. For this reason, it has become very important to determine the management and treatment procedures of dementia, to reduce the level of progression of the disease and to increase the quality of life of individuals exposed to the disease. The purpose of this study is to predict dementia and reveal the factors related to the disease with the deep learning approach.
In the current study, open-access dementia data, which includes the information of 376 patients, was used. Dementia prediction was made using the deep learning method. Model results were evaluated with accuracy, balanced accuracy, sensitivity, selectivity, positive predictive value, negative predictive value, and F1-score performance metrics. In addition, 10-fold cross-validation method was used in the modeling phase. Finally, variable importance values were obtained by modeling.
When the results are examined The highest metric values among the performance criteria achieved for group variable types were calculated for Demented; and were found that Accuracy, Sensitivity, Specificity, Positive predictive value, Negative predictive Value, and F1-score were 0.947, 0.946, 0.978, 0.966, 0.965 and 0.956 respectively.
As a result, when the findings obtained from this study were examined, the dementia dataset, which consisted of imaging data and information about patients with clinical data, was classified with high accuracy using the deep learning method. The risk factors for dementia were determined with the variable importance values obtained as a result of the model.

Keywords

Dementia, classification, machine learning, deep learning, variable importance.

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