This project uses machine learning and predictive analytics to support the early detection and risk prediction of breast cancer using publicly available or clinical healthcare datasets. Students will clean and analyse clinical and diagnostic health data, apply classification and risk modeling algorithms such as Logistic Regression, Random Forest, Support Vector Machines, or deep learning, and identify key clinical risk factors and diagnostic markers.
Honours and Masters project
Displaying 1 - 10 of 305 honours projects.
Health and Social Challenges of Refugee Populations in Australia: A Data-Driven Investigation (Honours)
Title: Health and Social Challenges of Refugee Populations in Australia: A Data-Driven Investigation.
Keywords: Refugees, health outcomes, social challenges, data integration, policy analysis
Project Description: Perform a comprehensive data-driven study on health and social challenges faced by refugee populations in Australia. The project integrates multiple datasets, applies advanced statistical analysis, and uses machine learning to detect patterns that inform policy and support services.
AI and Machine Learning for Early Prediction and Risk Stratification of Diabetes-Related Complications: Advancing Early Detection of Diabetic Foot Disease and Lower-Limb Amputation Risk for Timely Intervention
Background
Diabetes can lead to serious complications, including peripheral neuropathy, peripheral arterial disease and diabetic foot disease, which may increase the risk of foot ulcers and lower-limb amputation. Early identification of high-risk patients can support timely clinical intervention and preventive care.
Research Aim
AI and Machine Learning for Early Prediction and Risk Stratification of Endometriosis and Polycystic Ovary Syndrome: Advancing Earlier Diagnosis and Personalised Healthcare.
Background
Endometriosis and Polycystic Ovary Syndrome (PCOS) are complex conditions that can involve diverse symptoms and clinical characteristics. Delays in diagnosis and differences in symptom presentation highlight the potential value of data-driven approaches to support earlier identification and risk assessment.
Research Aim
AI-Enabled Early Detection and Risk Classification of Skin Cancer Using Machine Learning for Timely Diagnosis and Improved Patient Outcomes.
1. Background
Skin cancer, including melanoma, basal cell carcinoma (BCC), and squamous cell carcinoma (SCC), is a significant health concern in Australia. Early detection and accurate risk assessment can support timely clinical assessment and treatment.
2. Research Aim
This research aims to develop an AI and machine-learning framework for early detection, risk classification, clinical decision support, and forecasting of skin cancer.
3. Data and Methods
AI-Based Early Detection and Stage Prediction of Lung Cancer Using Machine Learning and Statistical Risk Modelling.
Lung cancer prediction research in computer science focuses on overcoming critical data engineering and algorithmic challenges—such as severe class imbalance in Electronic Health Records (EHR), high-dimensional tabular feature interactions, and the opacity of deep neural networks.
To address these AI-specific bottlenecks, this research proposes an end-to-end Predictive Artificial Intelligence pipeline designed for early risk scoring and multi-class stage prediction.
1. Research Objectives & Predictive AI Architecture
AI and Machine Learning for Early Detection, Classification and Prognostic Prediction of Brain Cancer to Support Timely Clinical Intervention.
Brain cancer—particularly High-Grade Gliomas (HGG) like Glioblastoma—remains one of the most lethal central nervous system malignancies, primarily due to non-specific early symptoms, rapid infiltrative growth, and late-stage clinical detection. Standard diagnostic pathways rely heavily on invasive biopsies and complex neuroimaging (e.g., MRI), which can delay intervention and lead to poor overall survival outcomes.
Early Prediction of ADHD Symptoms in Children Using Explainable Artificial Intelligence, Machine Learning and Statistical Risk Modelling.
This project uses explainable machine learning, predictive analytics, and statistical risk modelling to predict ADHD symptoms in children using publicly available or clinical healthcare datasets. Students will clean and analyse pediatric health and behavioral data, apply classification and risk stratification algorithms such as Logistic Regression, Random Forest, XGBoost, or multimodal deep learning, and identify key diagnostic risk factors using explainable AI methods.
Secure and Efficient Software Implementation of NIST post-quantum algorithms
The security threat by quantum computing to almost all currently used digital signatures was triggered by the discovery of Shor’s quantum algorithm, which efficiently breaks the two problems underlying the security of these schemes, namely integer factoring, and elliptic curve discrete logarithms (ECDLP). When quantum computers become widespread, all security for the current digital signatures that are widely used to secure a wide range of systems is lost.
Post-Quantum Digital Provenance
Digital provenance refers to the process of verifying and tracing the origins, lifecycle, and integrity of digital content. C2PA (Coalition for Content Provenance and Authenticity) is an initiative founded by multiple technology and media companies, aiming to address the increasing concern of misleading information and disinformation on the internet in the age of AI. The main objective of C2PA is to establish a standardized approach for digital content provenance, which essentially means tracing the origin and verifying the integrity of digital content.