Smart Health Predictions using Machine Learning

Summary  The "Smart Health Prediction Using Machine Learning" system, based on predictive modelling, predicts the disease of patients/users on the basis of the symptoms that the user provides symptoms as an input to the system. The application has three login options: user/patient login, do

Project Title

Smart Health Predictions using Machine Learning

Project Area of Specialization

Artificial Intelligence

Project Summary

Summary 
The "Smart Health Prediction Using Machine Learning" system, based on predictive modelling, predicts the disease of patients/users on the basis of the symptoms that the user provides symptoms as an input to the system. The application has three login options: user/patient login, doctor login, and admin login.The device analyses the symptoms given by the user/patient as input and provides the likelihood of the disease as output based on the prediction using the algorithm. Smart health predictions are made by the implementation of the Naïve Bayes Classifier,DecisionTree, K nearest neighbor and Random forest algorithm.  The Naïve Bayes Classifier measures the disease percentage probability by considering all its features that is trained during the training phase.Exact interpretation of disease data benefits early patient/user disease prediction and provides clear vision about the disease to the user. Random forest algorithm can be used for both classifications and regression task. It provides higher accuracy through cross validation. Random forest classifier will handle the missing values and maintain the accuracy of a large proportion of data.Decision trees use multiple algorithms to decide to split a node into two or more sub-nodes. The creation of sub-nodes increases the homogeneity of resultant sub-nodes. In other words, we can say that the purity of the node increases with respect to the target variable.  After a prediction, the user/patient can consult a specialist doctor using a chat consulting window. It uses machine learning algorithms and database management techniques to extract new patterns from historical data. The Forecast Accuracy can improve with the use of a machine learning algorithm and the user/patient will get fast and easy access to the application.

Project Objectives

Smart health prediction helps in the diagnosis of multiple diseases by analysing patient symptoms using a perfect fitting Machine Learning Algorithm technique. The framework predicts chronic diseases for a specific area and population. In this we also add a hardware that will helps in hospital (OPD). we add some sensors like oximeter, Temprature and Blood pressure that will also help for the patients as well as doctors.

Project Implementation Method

we use python language for coding that was completed in previous semester. Now in this Semester we buy raspberry pi to get code it and connect to screen that user input our symptoms and predict it. we also attached some sensors to raspberry pi like oximeter,  temperature and blood pressure sensor that also show the oxygen level of patient, temperature and blood pressure level.

Benefits of the Project

There are some advantages such as finding the nearest doctor option to find doctor near to our location.
Get treatment on time.
Rush down in hospitals OPD.
Moreover, while a healthcare professional and a machine learning algorithm will most likely achieve the same conclusion based on the same data set, using machine learning will get the results much faster, allowing to start the treatment earlier.
Another point for using machine learning techniques in healthcare is eliminating human involvement to some degree, which reduces the possibility of human error. This especially concerns process automation tasks, as tedious routine work is where humans err the most.

Technical Details of Final Deliverable

We use python language for coding in this we use four algorithms Decision Trees, k-NN, Naive Bayes and Random forest that was completed in previous semester. This project also work with one algorithm but we use four algorithms for comparing our results. It's of to user if they use only one algorithm or four algorithms. 

In this Semester we are working on hardware.

Components:

Raspberry pi

Touch screen

Sensors

Charger

Battery

Hardware Explanation:

Firstly we transfer our training model (code) to respberry pi and connect to touch screen now patients enter our symptoms on touch screen now there will four option in screen to predict diseases. As i mentioned above we use four algorithms so there will be four option to predict the diseases.

We also add some sensors to raspberry pi like oximeter, Temprature and Blood pressure. Now  they gives us information about patient oxygen level, temprature and blood pressure level. 

    

Final Deliverable of the Project

Hardware System

Core Industry

Medical

Other Industries

Health

Core Technology

Artificial Intelligence(AI)

Other Technologies

Others

Sustainable Development Goals

Good Health and Well-Being for People

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com