MITS5509: Heart Disease Prediction System Research Report

This report presents an intelligent heart disease prediction system (IHDPS) developed using data mining techniques. The system aims to analyze patient data to predict heart disease and suggest appropriate treatments, ultimately enhancing healthcare services. The report outlines the objectives of the system, including cost-effective healthcare, reduced diagnosis time, and improved disease prediction. It also details the benefits such as early disease detection, reduced costs, and effective treatment suggestions. The methodology involves Data Mining Extension (DMX) and CRISP-DM, with technologies like decision trees, neural networks, and Naive Bayes algorithms. The goal is to analyze patient data, determine relationships, and identify characteristics of different heart diseases to improve the efficiency of healthcare professionals. The limitations of the system, such as difficulties in analyzing large datasets and reliability concerns, are also acknowledged. The project is a part of the MITS5509 course and is designed to help students gain experience in researching a topic and writing a report relevant to the unit of study subject matter.

INTELLIGENT SYSTEM ANALYTICS
Intelligent heart disease prediction system using data mining techniques.

Purpose of this report is to elaborate a research which is based on
the application of data mining in the healthcare industry. Followed
by this concept an intelligent system has been approached in order
to address the healthcare requirements related to predicting the
disease and treatment for heart patients.

Data Mining..
Data mining is an concept in which large set of data is analyzed in
order to extract the a pattern with which the input data can be
compared with the purpose to predict the disease as well as the most
appropriate treatment for the identified disease.

Intelligent heart disease prediction system (IHDPS):
The approached system is entitled as the INTELLIGENT HEART DISEASE
PREDICTION SYSTEM, which will be developed based on the features of the
data mining. This system will holds the capabilities to effectively determine the
heart disease by analyzing patients data as well as it will also suggest most
effective treatment for the identified disease.

Objective(s):
Development of cost effective healthcare device.
Reduction of diagnosis time.
Effective disease prediction system to assist the doctors and
nurse.
Enhance the healthcare services.

Benefits of IHDPS:
Detects the heart disease.
Reduces the diagnosis time.
Reduces the disease prediction cost.
Effectively suggest the appropriate treatment for the identified disease.
This system is highly cost effective.

Limitation(s):
o There is a significant difficulties in analyzing large data set of
heart disease and symptoms.
o this is not completely reliable for healthcare field.

Methodology:
• Data mining extension- DMX
• CRISP-DM methodology

Technology(s):
• Data mining decision tree.
• Neural Network
• Navie Bayes Algorithm

Goal(s):
1. The patients data will be analyzed in order to determine the heart disease.
2. Analyze the provided input with respect to the predictive table.
3. Determine the relationship between the predictive table and input attributes.
4. Identification of the characteristics of different heart disease.
5. Determine the characteristics and the functions of the generated predictive
table.

• Considering the above mentioned aspect it can be stated that this
there is a huge impact on the healthcare industry as it will help the
doctors as well as the nurse to predict the possible disease as well
as followed by this is it will suggest effective medicines and
treatment to mitigate the risk.

THANK YOU 