The aim of the diploma thesis is to analyze the data concerning patients with Alzheimer’s disease and to use the predictive models constructed through machine learning methods.
The collected data was analyzed and the laws between attributes were defined.
The data attributes were presented in the form of an undirected graph.
The most relevant attributes were determined using the constructed models, the attributes that caused overfitting were eliminated.
The models thus obtained were tested through cross-validation and the accuracy of each model was calculated.
The constructed models and the comparisons between them showed that certain attributes were more distinctive than others.
These attributes would enable us to simplify and expedite the establishment of the diagnosis of the disease, conducting fewer tests.
Doctors deem the elimination of certain tests unreasonable, though, since a lot of information on the patient’s condition can be deduced from them.
We could, however, modify the sequence of the tests, which would lead to more rapid establishment of the diagnosis.
|
