Early Prediction of Dementia Using Feature Extraction Battery (FEB) and Optimized Support Vector Machine (SVM) for Classification

Abstract

Dementia is a cognitive disorder that mainly targets older adults. At present, dementia hasno cure or prevention available. Scientists found that dementia symptoms might emerge as early asten years before the onset of real disease. As a result, machine learning (ML) scientists developedvarious techniques for the early prediction of dementia using dementia symptoms. However, thesemethods have fundamental limitations, such as low accuracy and bias in machine learning (ML)models. To resolve the issue of bias in the proposed ML model, we deployed the adaptive syntheticsampling (ADASYN) technique, and to improve accuracy, we have proposed novel feature extractiontechniques, namely, feature extraction battery (FEB) and optimized support vector machine (SVM)using radical basis function (rbf) for the classification of the disease. The hyperparameters of SVM arecalibrated by employing the grid search approach. It is evident from the experimental results that thenewly pr oposed model (FEB-SVM) improves the dementia prediction accuracy of the conventionalSVM by 6%. The proposed model (FEB-SVM) obtained 98.28% accuracy on training data and a testingaccuracy of 93.92%. Along with accuracy, the proposed model obtained a precision of 91.80%, recall of86.59, F1-score of 89.12%, and Matthew’s correlation coefficient (MCC) of 0.4987. Moreover, the newlyproposed model (FEB-SVM) outperforms the 12 state-of-the-art ML models that the researchers have recently presented for dementia prediction.