]]> The methodology involves preprocessing the dataset to handle missing values, normalize input features, and encode categorical variables. Various classification algorithms were explored, including Logistic Regression, Random Forest, Support Vector Machine (SVM), and Gradient Boosting, to determine the most effective model. Model performance was evaluated using standard metrics such as accuracy, precision, recall, and F1-score through cross-validation techniques to ensure robustness.
Initial results indicate that ensemble methods, particularly Random Forest and Gradient Boosting, significantly outperform other models, achieving an accuracy of over 96%. These findings suggest that machine learning techniques can play a crucial role in assisting medical professionals with early diagnosis, thereby contributing to timely treatment and improved patient outcomes.
In conclusion, this project demonstrates the effectiveness of supervised machine learning algorithms in medical data analysis and highlights the potential of data-driven solutions for real-world health challenges. Future improvements may involve integrating additional medical features and deploying the model in a web-based diagnostic tool for practical use.
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