Diagnosis and Prediction of Alzheimer’s Disease by Chernoff Faces

Multivariate data analysis encompasses many statistical techniques used to analyze more than two variables simultaneously. These techniques allow a better understanding of data concerning specific business or real-world scenarios. However, since it has a more complex structure than bivariate analysis, applying this method and interpreting the results are complicated. Chernoff’s faces are used to represent this complex structure more understandably. Chernoff faces representing multivariate data in the form of a human face. Parts such as eyes, ears, mouth, and nose; treat each variable differently according to its shape, size, placement, and orientation. Chern of faces makes it easy to analyze data visually. Because Chernoff’s faces are closer to human visual perception, it allows the detection of patterns, trends, and relationships in the dataset. Alzheimer’s disease is a type of brain disease. It distorts the human face shape. The shapes of the formed face show the relationships between the variables. In practice (Educ, ses, MMSE, cdr, eTIV, nWBV, asf, age), the probability of people having Alzheimer’s disease is determined according to their trait values. In this study, using these features, Chernoff faces of humans were created using an Alzheimer’s patient data set from the literature. According to these face shapes, people who are likely to have Alzheimer’s disease in the future have been tried to be predicted.