Machine Learning for Healthcare Applications. Группа авторов. Читать онлайн. Newlib. NEWLIB.NET

Автор: Группа авторов
Издательство: John Wiley & Sons Limited
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Жанр произведения: Программы
Год издания: 0
isbn: 9781119792598
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i.e., Decision Tree, Bagging Decision Tree, Gaussian Naïve Bayes Classifier, Kernel SVM and Random Forest Classifier.

Photo depicts images used for visual evaluation. Photo depicts EEG signal for a product with corresponding Brain map and choice label.

      We compiled all participant’s EEG data into a single file called as “Master file” with appropriate like=1/dislike=0 labelling for all rows. We observe here that Kernel SVM has the highest achieved accuracy followed by Decision Tree whereas all other 3 produce near about close results.

Bar chart depicts accuracy for all users (compiled). Graph depicts individual result of each algorithm. Bar chart depicts result of 25-users with different algorithms.

      3.5.2 Comparative Results Analysis

      In this study, we applied our knowledge of EEG data and Machine Learning to cohabit in a system for correctly analyze and predict the consumer’s choice when surveying different brands of same type of products. We had 25 males perform this initial study and it resulted in a viable feasibility for developing solutions using EEG data to enhance productivity, cut down on losses and shifting the paradigm of marketing to new heights. We have noticed that on a user-level Kernel SVM has performed better than others in majority of the cases for identifying like/dislike. It has also recorded the highest accuracy in Master file run of 56.2% among others. We have observed that Kernel SVM: Sigmoid is significant to our study and we shall try different kernels in this form to test better results.

Bar chart depicts result of 25-users compared with different algorithms.

Schematic illustration of approximate brain EEG map for dislike state. Schematic illustration of approximate brain EEG map for like state.

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