Machine Learning

Health Care Improvement using Machine Learning

There are various potential projects in healthcare that are based on machine learning algorithms. In this project, we will discuss the heart-related disease diagnosis application, which is built with the concept of data analysis and machine learning. There is a great demand for this project in the real world, also, as doctors throughout the world want to detect the disease accurately.

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What are the objectives of this application?

This application is based on artificial intelligence and data analysis, which can also be used in various other fields in medical technology. This system is used to detect diseases related to cardiac muscles. It uses the data mining technique, which is considered to be accurate, among other techniques. The application takes input parameters like blood sugar, sex, ECG, blood pressure, and age. It is widely used in the medical field by doctors, patients, and students.

What are the methodology and hardware used?

A machine learning algorithm is used, which is based on the java open data source (WEKA), which uses the data mining technique. Arduino based microcontroller is used to monitor continuous cardiac muscle signals. The signals are analyzed to give accurate results. The data is then compared to the huge data set available, and it is studied by algorithms to find patterns that match.

This application will also need to be kept updated regularly by the doctors throughout as it helps the application to learn. The Arduino microcontroller board is used to calculate the signals. A heartbeat sensor is also required, which monitors the heart every now and then. The signals are equivalent pulses, and it gives the output of the heartbeat. An electric buzzer is used to give alert sounds whenever necessary.

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Temperature and humidity sensor studies the weather conditions as it also affects the heartbeat signals. Many algorithms can be used to implement the project, such as MATLAB, WEKA, TANAGRA, etc.

Project Implementation

The first step is the input of all the attributes which are necessary for the calculations. The data will be then sent to the microcontroller, which will compare it with the huge dataset. The microcontroller also considers all the other factors, such as temperature and humidity, to give accurate results. The output will be from 0-4 in which 0 stands for No heart disease, and 4 is the worst case.

The cloud server provides the user with the data set, which is updated and regulated. The alert signals can be used to notify the patient for a worst-case or no disease.


The results displayed at the end are used by the patients and doctors. The results will be simple to understand; it is advised to keep it numeric. The application will also suggest ideas for various other projects in the healthcare department.

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Health Care Improvement using Machine Learning
Skyfi Labs Last Updated: 2021-06-16

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