Migliorini, Giulia (A.A. 2024/2025) Artificial intelligence in healthcare: comparing classification models for disease diagnosis. Tesi di Laurea in Artificial intelligence and machine learning, Luiss Guido Carli, relatore Giuseppe Francesco Italiano, pp. 40. [Bachelor's Degree Thesis]

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Abstract/Index

Introduction to challenges and potential of AI in breast cancer diagnosis. The problem and its relevance. The role of artificial intelligence. State of the art and limitations. Artificial intelligence and machine learning. Goals of AI. Machine learning. Learning paradigms. Logistic regression, artificial neural networks and support vector machines. Artificial intelligence and machine learning in healthcare. Innovating diagnostics and patient care. Enhancing virtual care and electronic health records. Navigating ethical and social challenges. Promoting fairness and reducing bias. Driving research, patient engagement and rehabilitation. Looking ahead: opportunities and challenges. Innovations in predictive medicine: the impact of AI and ML. Predictive modeling in clinical contexts. Deep learning and medical data interpretation. Natural language processing (NLP) in healthcare. Personalization and risk stratification. AI for resource allocation and public health surveillance. Outlook and clinical integration. Exploring ML applications in breast cancer prognosis and prediction. Breast cancer and the role of machine learning. Literature review: existing studies and techniques. Breast cancer datasets: characteristics and justification. Performance and comparison of machine learning models. Challenges and limitations in previous work. Data analysis and predictive modeling for breast cancer prognosis. Analysis of a breast cancer dataset: a personal study. Key insights from data exploration. Visualizing mortality and survival patterns. Preprocessing and model evaluation. Distribution analysis of key numeric features. Analysis of categorical features and their relation to survival. Correlation heatmap: uncovering relationships between key variables. Multivariate analysis of predictive features.

References

Bibliografia: pp. 37-39.

Thesis Type:	Bachelor's Degree Thesis
Institution:	Luiss Guido Carli
Degree Program:	Bachelor's Degree Programs > Bachelor's Degree Program in Management and Computer Science, English language (L-18)
Chair:	Artificial intelligence and machine learning
Thesis Supervisor:	Italiano, Giuseppe Francesco
Academic Year:	2024/2025
Session:	Autumn
Deposited by:	Alessandro Perfetti
Date Deposited:	29 Apr 2026 12:48
Last Modified:	29 Apr 2026 12:48
URI:	https://tesi.luiss.it/id/eprint/45585

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