Multiple Disease Prediction Using Machine Learning

Authors- Associate Professor Dr.R.Jayaraj, Kirubkaran A

Abstract-– The global burden of disease continues to be a major contributor to disability and death, largely due to the challenges posed by disease prediction. This is primarily because of the overlapping nature of symptoms between different diseases, making accurate and timely diagnosis difficult. Current healthcare systems often focus on predicting and diagnosing a single disease at a time, which limits their scope and ability to identify multiple conditions concurrently. This work aims to design and develop an intelligent multiplatform software system capable of simultaneously predicting various diseases, utilizing modern machine learning techniques such as Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks. The integration of Binary Equivalent Simplification Optimization (BESO) in the system ensures that the model is both computationally efficient and highly accurate. The approach involves several key stages, including data collection and preprocessing, feature extraction through CNN, sequence modeling using BiLSTM, and optimization through BESO. CNNs are employed to capture critical features from the input data, which are then processed by BiLSTM to account for the temporal dependencies in disease progression. This system is expected to significantly reduce intervention costs, improve diagnostic accuracy, and ultimately enhance patient quality of life by enabling early, simultaneous detection of multiple diseases. By enabling the simultaneous prediction of multiple diseases, healthcare providers can more accurately identify at-risk patients, reduce the time spent on diagnosis, and optimize treatment plans. This system can also be particularly beneficial in resource-limited settings, where quick, accurate predictions are crucial but the availability of specialist care may be restricted. Moreover, as the software is designed to be a web-based application, it ensures wide accessibility and ease of use for both healthcare professionals and patients, making it a versatile solution for modern healthcare challenges. The long-term goal is to scale the system, incorporating additional diseases and datasets, and continuously refine its algorithms to enhance predictive power and healthcare outcomes on a global scaleelection process.

DOI: /10.61463/ijset.vol.13.issue2.289