Through the analysis of patient data, our objective is to create models that can precisely forecast the status of heart disease, thereby watchting in the early detection and treatment of the condition.
Methodology:
- Use a collection of data on heart disease including factors including gender, age, cholesterol levels, and blood pressure
- Data preprocessing are used to clean the dataset, adding in missing values, and standardizing features help to prepare the data for model training.
- Apply several classification techniques including Logistic Regression, a decision tree, Random Forest as well as Gradient
- Every model is trained on a dataset and their hyperparameters are adjusted to attain best
- Measuring criteria including precision, recall, precision, accuracy, F1-score, and ROC- AUC helps them to evaluate the performance of a model.
The results demonstrate that in heart disease prediction, the Gradient Boosting classifier displayed the highest degree of accuracy. This work highlights the need of carefully selecting and evaluating models as it shows the potential of machine learning in the area of healthcare for timely detection of diseases.