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CHAPTER 6

CONCLUSION AND RECOMMENDATIONS

This comprehensive research project embarked on a critical mission to revolutionize

the domain of stroke risk assessment through the integration of advanced machine
learning techniques. The study dissected the existing methodologies in stroke risk

assessment, identifying their key inadequacies and limitations. In response, it

developed a web application with innovative dashboard, backed by a machine learning
tool, to offer a stroke risk prediction model. The culmination of this project was an

extensive user acceptance testing phase using TAM Model and metrics evaluation for
the prediction model, which provided crucial insights into the efficacy and reception

of the developed system.

6.1 Objective Achievement

This project successfully met its objectives, demonstrating a notable

advancement in stroke risk assessment. A comprehensive analysis of existing
methods revealed significant gaps, particularly in personalization and

accuracy, highlighting the potential of machine learning to enhance these
areas. The development of the dashboard and the machine learning-powered

tool marked a key achievement, offering a user-friendly interface for more
accurate risk predictions. The effectiveness of this innovative system was

affirmed through user acceptance testing, which showcased high levels of

user satisfaction and engagement. This tripartite approach effectively bridged
the gap between advanced technological capabilities and practical healthcare

needs, setting a new standard in predictive health analytics.

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