Intelligent Heart Disease Prediction System using Data Mining Techniques

ABSTRACT:

The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not “;mined”; to discover hidden information for effective decision making. Discovery of hidden patterns and relationships often goes unexploited. Advanced data mining techniques can help remedy this situation. This research has developed a prototype Intelligent Heart Disease Prediction System (IHDPS) using data mining techniques, namely, Decision Trees, Naive Bayes and Neural Network. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. IHDPS can answer complex “;what if”; queries which traditional decision support systems cannot. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood of patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease, to be established. IHDPS is Web-based, user-friendly, scalable, reliable and expandable. It is implemented on the .NET platform.

PROJECT OUTPUT VIDEO:

EXISTING SYSTEM:

Clinical decisions are often made based on doctors’ intuition and experience rather than on the knowledge rich data hidden in the database.

This practice leads to unwanted biases, errors and excessive medical costs which affects the quality of service provided to patients.

There are many ways that a medical misdiagnosis can present itself. Whether a doctor is at fault, or hospital staff, a misdiagnosis of a serious illness can have very extreme and harmful effects.

The National Patient Safety Foundation cites that 42% of medical patients feel they have had experienced a medical error or missed diagnosis. Patient safety is sometimes negligently given the back seat for other concerns, such as the cost of medical tests, drugs, and operations.

Medical Misdiagnoses are a serious risk to our healthcare profession. If they continue, then people will fear going to the hospital for treatment. We can put an end to medical misdiagnosis by informing the public and filing claims and suits against the medical practitioners at fault.

PROPOSED SYSTEM:

This practice leads to unwanted biases, errors and excessive medical costs which affects the quality of service provided to patients.

Thus we proposed that integration of clinical decision support with computer-based patient records could reduce medical errors, enhance patient safety, decrease unwanted practice variation, and improve patient outcome.

This suggestion is promising as data modeling and analysis tools, e.g., data mining, have the potential to generate a knowledge-rich environment which can help to significantly improve the quality of clinical decisions.

The main objective of this research is to develop a prototype Intelligent Heart Disease Prediction System (IHDPS) using three data mining modeling techniques, namely, Decision Trees, Naïve Bayes and Neural Network.

So its providing effective treatments, it also helps to reduce treatment costs. To enhance visualization and ease of interpretation.

MODULES:

Admin
Users
Disease Prediction

MODULE DESCRIPTIONS:

Admin:

In this Module, Admin can add the Doctor details and the Training datasets. The doctor detail consists of fields such as name, contact and specialist in. Similarly Training Datasets consists of previous analyzed data’s of patient’s history, such as Blood Sugar, Blood Pressure, Heart Rate and etc.

Users:

Intended Users who wants to predict the possibility of disease they are suffered, they will give the information about their conditions. The System will show the analyzed Results from the training datasets to the Users.

Disease Prediction:

Disease Prediction will analyzed from training datasets through Data mining techniques:

K-mean clustering: .The k-means algorithm is an evolutionary algorithm that gains its name from its method of operation. The algorithm clusters information into k groups, where k is considered as an input parameter. It then assigns each information to clusters based upon the observation’s proximity to the mean of the cluster.

Prophecy of heart disease using K – Means clustering techniques:

The algorithm arbitrarily selects k points as the initial cluster centers (“means”).
Each point in the dataset is assigned to the closed cluster, based upon the Euclidean distance between each point and each cluster center.
Each cluster center is recomputed as the average of the points in that cluster.
Steps 2 and 3 repeat until the clusters converge. Convergence may be explained differently depending upon the performance, but it regularly explains that either no observations change clusters when steps 2 and 3 are repeated or that the changes do not make a material difference in the definition of the clusters.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium i3 Processor
Hard Disk : 500 GB..
Monitor : 15’’ LED
Input Devices : Keyboard, Mouse
RAM : 4 GB.

SOFTWARE REQUIREMENTS:

Operating system : Windows 10/11.
Coding Language : C#.net.
Frontend : ASP.Net, HTML, CSS, JavaScript.
IDE Tool : VISUAL STUDIO.
Database : SQL SERVER 2005.

Dotnet Projects

Intelligent Heart Disease Prediction System using Data Mining Techniques