The Ministry of Health Malaysia (MOH) is responsible for delivering comprehensive healthcare services to the nation’s population. As part of its mandate, the MOH is actively pursuing digital health transformation, which includes data-driven approaches to disease prevention and patient care. 

MOH aims to improve the nation’s health through better care and disease prevention. This project supports MOH’s goals by using data mining to predict stroke risks early, helping doctors take preventive action before a stroke happens.

By using this system, MOH and public hospitals can:

• Identify high-risk patients earlier for faster intervention.

• Use hospital resources better, such as staff and screening tools.

• Save costs by avoiding expensive emergency care.

• Create smarter health policies using real data.
  
  

This project also reflects the growing role of AI and digital tools in healthcare—making Malaysia’s healthcare system more modern and proactive.

Stroke is one of the top causes of death and disability in Malaysia, especially among younger people. Many cases are only detected too late—when treatment is less effective and more costly.

Although stroke risk factors like high blood pressure, diabetes, and smoking are widely known, hospitals often lack smart tools to spot high-risk patients early. Manual screenings aren’t enough for large populations.

This project uses machine learning to turn patient data into early warnings. By predicting stroke risk before symptoms appear, healthcare providers can shift from late treatment to early prevention—saving lives and reducing costs.

Ultimately, we aim to:

• Predict if a patient is likely to experience a stroke—focusing on catching actual cases

• Identify individuals at highest risk to support early medical intervention

• Help healthcare providers classify patients into low, medium, and high-risk groups for better-targeted care