A Web-Based Model to Predict a Neurological Disorder Using ANN

Abstract

Dementia is a condition in which cognitive ability deteriorates beyond what can be anticipated with natural ageing. Characteristically it is recurring and deteriorates gradually with time affecting a person's ability to remember, think logically, to move about, to learn, and to speak just to name a few. A decline in a person's ability to control emotions or to be social can result in demotivation which can severely affect the brain's ability to perform optimally. One of the main causes of reliance and disability among older people worldwide is dementia. Often it is misunderstood which results in people not accepting it causing a delay in treatment. In this research, the data imputation process, and an artificial neural network (ANN), will be established to predict the impact of dementia. based on the considered dataset. The scaled conjugate gradient algorithm (SCG) is employed as a training algorithm. Cross-entropy error rates are so minimal, showing an accuracy of 95%, 85.7% and 89.3% for training, validation, and test. The area under receiver operating characteristic (ROC) curve (AUC) is generated for all phases. A Web-based interface is built to get the values and make predictions.

Keywords: brain disorder; data imputation; dementia; performance measures; scaled conjugate gradient.

Figure 1

Bibliometric network for dementia.

Figure 2

Entire workflow.

Figure 3

Feature statistics.

Figure 4

Sample data.

Figure 5

Sieve—group vs. age.

Figure 6

Sieve—group vs. M/F.

Figure 7

Model summary.

Figure 8

Cross-entropy and error.

Figure 9

Validation performance.

Figure 10

Training ROC.

Figure 11

Validation ROC.

Figure 12

Test ROC.

Figure 13

All ROC.

Figure 14

Web interface.