Efficient Machine Learning & Deep Learning Approaches for Disease Detection

This study presents machine learning-based approaches for early disease identification, focusing on four critical health conditions. Medical image analysis and machine learning have significantly improved disease detection and diagnosis across various conditions, including breast cancer, heart failure, pneumonia, and diabetes. Breast cancer detection uses mammography preprocessing and tumor segmentation, classified with a Random Forest (95% accuracy). Heart failure prediction integrates Random Forest [RF], Gaussian Naïve Bayes, Decision Tree [DT], and k-NN (87% accuracy) on multiple datasets. Pneumonia detection applies CNN-based feature extraction with supervised classification, leveraging transfer learning. Diabetes prediction employs Multilayer Perceptron, k-NN, and Random Forest, achieving 98.8% accuracy. These methods demonstrate ML’s potential for precise, early diagnosis, improving medical decision-making and timely interventions.