Comparative Study to Measure the Performance of Commonly Used Machine Learning Algorithms in Diagnosis of Alzheimer's Disease

kumar, Neeraj;manhas, Jatinder;sharma, Vinod

  • Received : 2019.04.30
  • Accepted : 2019.05.27
  • Published : 2019.06.30


In machine learning, the performance of the system depends upon the nature of input data. The efficiency of the system improves when the behavior of the input data changes from un-normalized to normalized form. This paper experimentally demonstrated the performance of KNN, SVM, LDA and NB on Alzheimer's dataset. The dataset undertaken for the study consisted of 3 classes, i.e. Demented, Converted and Non-Demented. Analysis shows that LDA and NB gave an accuracy of 89.83% and 88.19% respectively in both the cases whereas the accuracy of KNN and SVM improved from 46.87% to 82.80% and 53.40% to 88.75% respectively when input data changed from un-normalized to normalized state. From the above results it was observed that KNN and SVM show significant improvement in classification accuracy on normalized data as compared to un-normalized data, whereas LDA and NB reflect no such change in their performance.


Alzheimer's disease;KNN;Machine learning;Neurodegeneration