Using EEG to Detect Eye Movement

In this paper, we show that it is possible to use EEG data to detect eye movement using machine learning. By recognizing eye movement through EEG results, our goal is to help individuals with disabilities better control object movement and perform daily activities independently. This is especially important as many disabled individuals rely on assistance from others for their daily needs, which can be burdensome for the person providing help. To achieve these objectives, we trained different machine learning models using a data set of eye-state classification from Kaggle. We analyzed the results to assess the accuracy of a KNN (K Nearest Neighbors) model. With the model achieving an accuracy of 95.23% in detecting eye movement in patients. These findings suggest that the model could be effectively utilized in the future, with further research to assist individuals with disabilities. Overall, our research suggests that it is possible to recognize eye movement through EEG results reliably. Further research in this area could lead to the development of more effective and personalized interventions for individuals with poor hand-eye coordination.

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Skin Cancer Detection

The goal of this research project is to predict whether or not a patient has skin cancer through a machine learning model that is developed from an image dataset. Skin cancer is extremely dangerous, as over 9500 people in the US are diagnosed with it daily. If detected early, patients will have a more likely chance of survival. I tested an MLP Classifier, Decision Tree Regressor, a Logistic Regression Model, and a KNN Model to compare various results and ultimately determine the best accuracy. The MLP Classifier had a 74.5% accuracy, the Decision Tree Regressor had a 74.1% accuracy, the Logistic Regression Model had a 68.8% accuracy, and the KNN Model had a 74.6% accuracy (all testing). We can see that the MLP Classifier, Decision Tree Regressor, and the KNN Model had around the same accuracy while outperforming the Logistic Regression Model. However, when comparing training data, there seems to be a large overfitting problem with most of the models.

Jaida G.

Mentored by Odysseas Drosis

Predicting Drug-Drug Interaction Severity Using Network Characteristics

Drug-drug interactions (DDIs), which can add to or diminish the effect of one drug or impact the metabolism of one drug, have harmful effects on health in patients that take multiple drugs. Testing for DDIs is slow and costly, so computational models have recently been used to predict them. Network information is useful in describing a drug’s known interactions and mechanisms to determine whether two drugs could be interacting. This research explores various machine learning models to predict the severity of unknown interactions of existing drugs (major, minor, or moderate), using the DDInter database.

Deetya N.

Mentored by Linda Banh

Optimizing Predictive Models for Healthcare: A Comparison of Personal and General Datasets in Coronary Artery Disease Risk Assessment

This research investigates how the choice of training population, personalized versus more general datasets, impacts the performance and generalizability of machine learning models for predicting Coronary Artery Disease risk. By comparing models trained on datasets of varying specificity using data from the UC Irvine Machine Learning Repository, the study explores trade offs between accuracy and broad applicability to improve strategies for early CAD risk assessment and prevention.

Isaac G.

Mentored by Raphi Kang

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Using EEG to Detect Eye Movement

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Research Paper