Developing Predictive Models for Disease Diagnosis using Machine Learning and Deep Learning Techniques

This paper discusses the growth of prediction replicas for illness forecasting based on advanced machine learning (ML) and deep learning (DL) methodologies. With healthcare's growing adoption of digital transformation, the demand for precise, efficient, and scalable diagnostic solutions has also increased. It contrasts the latest DL architectures like convolutional neural networks (CNNs) and recurrent neural networks (RNNs). We focus on preprocessing medical datasets to improve model training and generalization, including missing data treatment, feature selection, and dimensionality reduction approaches such as principal component analysis (PCA). All the models are trained on publicly released datasets, making them reproducible and transparent. Evaluation is done using performance metrics such as correctness, exactness, memory usage, F1-score, and area under the curve (AUC-ROC). Journals with high citation counts, such as Nature, have published letters discussing the effectiveness of DL models, to the extent that DL models, especially CNNs, outperform traditional ML models for imaging data. In contrast, conventional ML methods provide better ways of handling structured clinical data. Cross replicas that combine the strengths of both ML and DL techniques hold promise in improving diagnostic accuracy and reducing false-positive rates. Models with a better fit can offer significant efficiency advantages.