Disease Prediction using EEG
Shaurya Kohli, Shreyas Chaudhry, Nehul Jindal, Santhi K
Introduction
For decades, researchers have been struggling to predict diseases using patients' treatment histories and health data by applying data mining and machine learning techniques. Previous works have used data mining techniques to predict the reoccurrence of specific diseases, control and progression of diseases. However, recent advancements in deep learning have led to a shift towards machine learning models that can learn complex patterns from raw data with minimal preprocessing and produce more accurate results. Big data technology has also contributed to disease prediction by automatically selecting relevant features from large amounts of data to improve the accuracy of risk classification. The main goal is to use machine learning to supplement patient care in healthcare and improve disease diagnosis and prediction. Predictive analysis with the help of multiple efficient machine learning algorithms helps in accurate disease prediction and patient treatment.
The use of algorithms to predict diseases based on patient symptoms can provide accurate and cost-effective treatment. With the healthcare system overloaded and becoming more expensive due to an increasing number of patients and diseases annually, disease prediction through algorithms can be a useful tool. In a project that used four different algorithms to predict diseases based on patient symptoms, an accuracy of 92-95% was achieved. Such a system has great potential for future medical treatments and an intelligently designed interface encourages interaction with the framework. The results of the study and project were visualized and presented. Nowadays, doctors are using various scientific technologies and methods to diagnose not only common illnesses but also fatal diseases. The success of treatment is often attributed to accurate and proper diagnosis. The results of the study and project were visualized and presented. Nowadays, doctors are using various scientific technologies and methods to diagnose not only common illnesses but also fatal diseases. The success of treatment is often attributed to accurate and proper diagnosis. The project of disease prediction using machine learning has been developed to detect general diseases in the early stages. In today's competitive economic environment, people have become so busy that they tend to ignore their health, which can result in harmful diseases later on. Research shows that 40% of people ignore common diseases, leading to severe health problems. This ignorance is mainly due to laziness in consulting a doctor, as people have very busy schedules and no time to take appointments and visit a doctor. The statistics also show that 70% of people in India suffer from general diseases, and 25% of people face death due to early ignorance.
