In this project, I explore the fascinating world of predictive analytics in healthcare by leveraging machine learning to predict the mortality risk of patients with heart failure.
Cardiovascular diseases (CVDs) claim millions of lives annually, making them a global health concern. Heart failure, a common consequence of CVDs, presents an opportunity for early intervention. In this project, I utilize a dataset from Kaggle, encompassing 12 features such as age, anemia, and serum creatinine, to develop a machine learning model capable of predicting mortality based on these factors.
- Number of Entries: 299
- Features: Age, Anaemia, Creatinine Phosphokinase, Diabetes, Ejection Fraction, High Blood Pressure, Platelets, Serum Creatinine, Serum Sodium, Sex, Smoking, Time.
- Target Variable: DEATH_EVENT (0: No Death, 1: Death)
As cardiovascular diseases remain a leading cause of death globally, there's a critical need for predictive models to aid in early detection. This project aims to contribute to this goal by training a neural network model to identify individuals at high risk.
I've trained a neural network model using features like age, ejection fraction, and serum creatinine. The model's performance is evaluated on a test set, and key metrics such as accuracy, precision, recall, and F1-score are considered to assess its effectiveness.
After 100 epochs of training, the model achieved an accuracy of approximately 92.47% on the training set and demonstrated promising results on the test set with an accuracy of 73.33%. The model's precision, recall, and F1-score metrics are also provided to offer a comprehensive understanding of its performance.