Assessing the Best Fit model and factor that contribute to the Prediction of Heart Attack Risk in Global Populations.
#Project overview Research aims to understand and combat the global challenge of heart attacks. The "Heart Attack Risk Prediction Dataset" is an excellent resource for study and analysis in healthcare and medical data science. The dataset's main goal is to predict the risk of heart attacks in individuals based on a variety of health-related characteristics. It makes a substantial contribution to our understanding of heart attack risk factors and the advancement of healthcare preventative approaches. This set of data, which includes 8763 information from patients worldwide, comes to an end in a substantial binary classification component that indicates the existence or failure of a heart attack risk, giving a complete resource for predictive analysis and cardiovascular health research. This multidisciplinary initiative improves our understanding of cardiovascular health by changing risk assessment and treatment for overall wellness. The project's main goal is to thoroughly investigate the variables influencing the likelihood of having a heart attack by using cutting-edge analytical and predictive modeling methods. This project is a major step toward changing the way we approach cardiovascular health risks and preventive healthcare strategies, not just an exercise in data analysis.
Objective Exploration: Our project is dedicated to investigating the myriad factors that may influence the risk of heart attacks, employing analytical models to predict susceptibility.
Predictive Power: By integrating best model that correctly predict and give insights, heart attack risk prediction, which is pivotal for preemptive healthcare measures.
Research Contribution: This study is poised to make a valuable contribution to the field of health risk prediction research.
https://www.kaggle.com/datasets/iamsouravbanerjee/heart-attack-prediction-dataset/data
S- Specific, M-Measurable, A-Achievable, R-Relevant , T-Timing Some SMART questions we are interested in answering are:
- Which factors most correlate with heart attack risk across various groups, and what patterns contribute to this risk?
- How do cholesterol levels and obesity relate, and do they impact heart attack risk?
- How do age and gender influence heart attack risk, and are there specific patterns related to age or gender?
- Which model best predicts of heart attack risk?
1.Aaron Young 2.Modupeola Fagbenro 3.Bharath Genji Mohana Ranga 4.Kundana chowdary Cherukuri.