Evolutionary Wavelet Neural Network Ensembles for Breast Cancer and Parkinson's Disease Prediction

1. Introduction

Breast cancer and Parkinson's complaint are two current and life- altering medical conditions that significantly impact the lives of millions of individuals worldwide. The early and accurate opinion of these conditions is pivotal for effective treatment and better patient issues. Accordingly, there's a growing interest in developing advanced prophetic models to support the early discovery and opinion of breast cancer and Parkinson's complaint. In recent times, machine literacy and artificial neural networks have surfaced as important tools for medical opinion and prediction. These styles have demonstrated their eventuality to dissect complex medical data and excerpt meaningful perceptivity that can prop healthcare professionals in making informed opinions. Still, challenges remain in achieving high vaticination delicacy, especially when dealing with intricate and miscellaneous datasets. This paper presents a new approach to address these challenges by introducing an ensemble of Evolutionary Wavelet Neural Networks(E-WNNs) for the prediction of breast cancer and Parkinson's complaint. Our methodology leverages the strengths of both evolutionary algorithms and sea metamorphoses to enhance the prophetic performance of neural networks. Wavelet metamorphoses allow for effective point birth from medical data, landing both original and global patterns, while evolutionary algorithms optimize the armature and parameters of neural network ensembles to ameliorate delicacy and robustness. Table 1 represents the breast cancer diseases identification parameters. Breast cancer and Parkinson's complaint are chosen as the target conditions due to their significant clinical applicability. Breast cancer remains one of the leading causes of cancer- related deaths among women encyclopedically, while Parkinson's complaint presents unique individual challenges, frequently taking a multidimensional analysis of clinical data. Table 1 represents the breast cancer disease identification parameters.