Omni-Health

Introduction:

Many of the existing machine learning models for health care analysis are concentrating on one disease per analysis. For example first is for Diabetes analysis, one for Heart analysis, one for Lung diseases like that. If a user wants to predict more than one disease, he/she has to go through different sites. There is no common system where one analysis can perform more than one disease prediction. Some of the models have lower accuracy which can seriously affect patients’ health. supervised machine learning (ML) algorithms have showcased significant potential in surpassing standard systems for disease diagnosis and aiding medical experts in the early detection of high-risk diseases. In this literature, the aim is to recognize trends across various types of supervised ML models in disease detection through the examination of performance metrics. The most prominently discussed supervised ML algorithms were Na¨ıve Bayes (NB), Decision Trees (DT), K-Nearest Neighbor (KNN). As per findings, Support Vector Machine (SVM) is the most adequate at detecting kidney diseases and Parkinson’s disease. The Logistic Regression (LR) performed highly at the prediction of heart diseases. Finally, Random Forest (RF), and Convolutional Neural Networks (CNN) predicted in precision breast diseases and common diseases, respectively.

Highlights Of The Project:

Disease prediction by patient health data and treatment history through machine learning techniques and applying data mining is a continuing struggle for past decades.The Classification algorithms is generally used to predict diseases analyzing patient health data and treatment history. Prediction by a traditional sickness threat model typically involves a machine learning and some supervised algorithm which uses guidance data with the label for the preparation of the models.

Uses Cases Contain:

Preventative care: Predicting disease epidemic on both the personal and the public level. Diagnostic care: Repeatedly classified illustration data, such as x-rays or scans, etc. Insurance: Adjust premiums of coverage based on openly available risk factors.

As hospitals maintain to modernize patient reports and gather more grainy health information, Prediction System Machine Learning Project is the arrival of low-hanging fruit opportunity for health scientists to make a difference.

Algorithm Used to Predict Disease

Classification Algorithms Decision Tree Support vector Machine(SVM) Logistic Regression(LR)

Static Pages and other sections:

These static pages will be available in project Disease Prediction System Home Page with good Ul Home Page will contain an animated slider for images banner About us page will be available which will describe about the project Contact us page will be available in the project

Technology Used in the project Disease Prediction System

HTML: Page layout has been designed in HTML CSS CSS has been used for all the desigining part. JavaScript: All the validation task and animations has been developed by JavaScript Python: All the business logic has been implemented in Python MySQL: MySQL database has been used as database for the project Django: Project has been developed over the Django Framework Streamlit: Python library

Summary-

The use of different ML algorithms enabled the early detection of many maladies such as heart, kidney, breast, and brain diseases. In future work, the creation of more complex ML algorithms is much needed to increase the efficiency of disease prediction.