An implementation analysis of a machine learning algorithm on eye-tracking data in order to detect early signs of dementia

Sammanfattning: This study aims to investigate whether or not it is possible to use a machine learning algorithm on eye-tracking data in order to detect early signs of Alzheimer’s disease, which is a type of dementia. Early signs of Alzheimer’s are characterized by mild cognitive impairment. In addition to this, patients with mild cognitive impairment fixate more when reading. The eye-tracking data is gathered in trials, conducted by specialist doctors at a hospital, where 24 patients read a text. Furthermore, the data is pre-processed by extracting different features, such as fixations and difficulty levels of the specific passage in the text. Thenceforth, the features are applied in a naïve Bayes machine learning algorithm, implementing so called leave-one-out cross validation, under two separate conditions; using both fixation features and features related to the difficulty of the text and in addition to this, only using fixation features. Finally, the two conditions achieved the same results - with an accuracy of 64%. Thereby, the conclusion was drawn that even though the amount of data samples (patients) was small, the machine learning algorithm could somewhat predict if a patient was at an early stage of Alzheimer’s disease or not, based on eye-tracking data. Additionally, the implementation is further analyzed through the use of a stakeholder analysis, a SWOT-analysis and from an innovation perspective.