| dc.description.abstract | Early detection of heart strokes is crucial
for timely medical intervention and improved patient
outcomes. This research aims to develop a reliable and
accurate heart attack prediction model using machine
learning techniques on patient medical data. This
study has conducted exploratory data analysis (EDA)
on a Kaggle dataset, including variables such as age,
sex, blood pressure, BMI, cholesterol, and smoking
status. After preprocessing and cleaning the data, it
was evaluated several predictive models, including
decision trees, logistic regression, and some other
algorithms. Preliminary results indicate that systolic
and diastolic blood pressure significantly impact
stroke risk. To enhance the model accuracy and
robustness, future work may integrate fuzzy logic into
the prediction model. This study contributes to the
computing and medical domains by providing a
framework for effective prediction and insights into
key factors influencing heart attack risk, potentially
aiding early diagnosis and personalized treatment
plans. | en_US |
