ITDS | SPMS

Analysing The Prevalence And Treatment Gaps Of Mental Illnesses: Data-Driven Insights And Predictive Modelling

Project Description
Supervisor

Mental health illnesses such as bipolar disorder, depression, and anxiety are extremely common in a wide range of populations and pose a significant global public health risk. Despite the prevalence of these conditions, there is still a huge treatment gap, particularly in low- and middle-income countries where more than 80% of individuals do not have access to mental health services. The current study uses data-driven approaches and predictive modelling to investigate the incidence of mental health diseases as well as the variables that contribute to treatment gaps. This study employs machine learning techniques such as Random Forest and Support Vector Machines (SVM) to discover critical healthcare-related, socioeconomic, and demographic characteristics that exacerbate the gap in mental health treatment. To ensure trustworthy analysis, the study examines massive datasets gathered from international health organisations using data integration and cleaning approaches. Predictive models are designed to assess the potential of treatment gaps in specific groups. These models provide insightful data to healthcare practitioners and policymakers. The findings indicate the locations and people that are most vulnerable to receiving inadequate mental health treatment, and they argue that focused interventions and resource allocation can assist to eliminate these gaps. This study also emphasises the technical and ethical concerns connected with predictive modelling in mental health, including data quality and access to care equity. The findings of this study make critical recommendations for closing the mental health care gap and improving mental health outcomes everywhere.

Machine Learning: Machine Learning (ML) research in Computer Science and Information Technology focuses on the development of algorithms and models that enable computers to learn from data and improve their performance over time without being explicitly programmed. It is a subset of Artificial Intelligence that uses statistical techniques to give machines the ability to learn patterns, make decisions, and predict outcomes based on data. Supervised learning, a key area of ML research, involves training models on labeled data, where the input-output relationships are predefined. This method is widely used for tasks such as classification (e.g., spam detection) and regression (e.g., predicting house prices). Unsupervised learning, on the other hand, involves finding hidden patterns in data without predefined labels, with clustering and association being typical applications in areas such as customer segmentation and anomaly detection. Reinforcement learning is another area of ML that focuses on teaching agents to make decisions by interacting with their environment and receiving feedback in the form of rewards or penalties. It is often applied in robotics, game playing, and autonomous systems, where continuous learning and adaptation are required.

The main objective of the study is to present a comprehensive analysis of the treatment gaps for mental illnesses and their incidence. We will employ data-driven methods to find insights that can inform practice and policy.

2023/2024

Nov 19, 2024

DERRICK OSEI KWAME MICHELLE AMANYAME KYEI PRISCILLA NKUNO AMOAH GYAMFI JOSHUA

BSc Information Technology