Modeling Sleep Efficiency

This study explores how alcohol use, exercise, and smoking affect sleep quality by analyzing a dataset containing various lifestyle and sleep-related factors. Using a regression-based predictive model, the research identifies relationships between these behaviors and sleep outcomes, with the final linear regression model achieving a mean absolute error of 0.051.

I’ve always been interested in sleep and why some people tend to sleep better than others. I think it’s important to recognize what could be affecting a person’s sleep and what they can do to improve it. The goal of my research question, “How much do alcohol, exercise, and smoking affect sleep?” is to show how factors such as these can affect the quality of one’s sleep. I found a data set consisting of many factors that can affect sleep, as well as sleep quality. Using this data set, I built a model that allows you to input values for factors and come out with a sleep quality score. My model is a regression model, as it makes predictions through numerical data given to it. I tested out different models, settling on the most accurate one, Linear Regression, with a mean absolute error of 0.051.

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Stroke Susceptibility Prediction Using Artificial Neural Networks

Our research question is centered around predicting stroke susceptibility and determining which demographic and medical factors significantly increase or decrease that risk. Our results indicate the potentiality of the use of AI in determining stroke risk based solely on collected medical and lifestyle data.

Vinati P.

Mentored by Samuel Kwong

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

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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Modeling Sleep Efficiency

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Research Paper