This study aims to enhance medical diagnostics by integrating machine learning algorithms with optimization techniques. Specifically, we focus on improving the accuracy of algorithms like logistic regression, k-nearest neighbors (KNNs), support vector machines (SVMs), and neural networks through systematic parameter tuning. The diagnostic function \({\text {diagnosis}} = f(p_{1}, p_{2}, \ldots , p_{n_{f}})\) can be approximated using various techniques, such as neural networks and nonlinear regression models. To optimize the performance of these models, we extrapolate the function f to identify new potential optimal values. Our approach explores algorithmic parameter spaces, using computational graphs to project optimal characteristics. This data-driven framework not only enhances existing algorithms but also encourages the development of innovative, highly accurate diagnostic tools. We demonstrate the effectiveness of this approach through several case studies. For logistic regression, we fine-tune the regularization strength parameter (C). In KNN, we adjust the number of neighbors (n) and the power parameter (p) used in the Minkowski distance metric. For SVM, we optimize the kernel selection. Finally, we apply meta-learning techniques to further improve the models’ performance. These adjustments lead to significant improvements in diagnostic accuracy, illustrating the potential of our approach to contribute meaningfully to the field of medical diagnosis.