The Development of Mobile-Based Symptom Analysis for Early Detection of Diseases Using Hyper-Tuned C-Support Vector Classification Algorithm

One of the fundamental human needs that everyone should be able to access is healthcare, but most people are unable to do so for a variety of reasons, including remote areas, a lack of healthcare facilities, and a lack of financial assistance. In this study, the researchers aim to diagnose users' diseases associated with symptoms. To achieve this objective, the researchers built and leveraged an advanced predictive model upon the Hyper-Tuned C-Support Vector Classification Algorithm. This model served as the cornerstone for the analysis, harnessing the power of machine learning to accurately diagnose diseases. The researchers gathered secondary data from Kaggle, and have used rigorous performance metrics to test and analyze the accuracy and effectiveness of the model. The study showed a promising result for contributing to the early detection of diseases using machine learning methods.