Machine Learning Based Stream-Lit API Multi-Disease Detection

Liver disease and Parkinson's disease are two of the major and the common chronic diseases that affect millions of people around the world. Although early detection of these diseases can significantly enhance the treatment results, diagnosis can be difficult due to their complex and diverse symptomatology. This study aims to develop a predictive system that can correctly identify patients suffering with liver and Parkinson's disease, potentially improving early detection and diagnosis. The proposed system effectively trains and determines all basic machine learning Modules and Algorithms constitutes from logistic regression, naive bayes, random forest, decision trees and support vector machines, using a dataset of clinical and demographic information from patients with liver and Parkinson's disease. Then the dataset is preprocessed, and features are selected based on classification process. Furthermore, suggested model's functionality and precision is calculated with an individual's test and training data. Therefore, the proposed machine learning-based prediction approach has got the potential to improve the early identification of liver disease and Parkinson's disease, thereby impacting patient outcomes and healthcare expenses significantly. Future research could look towards integrating more data sources, such as genetic and imaging data, to improve the system's performance and diagnostic capabilities.