Disease Prediction System using Data Mining
Disease Prediction System using Data Mining
ABSTRACT:
The successful application of data mining in highly visible fields like e-business, commerce and trade has led to its application in other industries. The medical environment is still information rich but knowledge weak. There is a wealth of data possible within the medical systems. However, there is a lack of powerful analysis tools to identify hidden relationships and trends in data. Heart disease is a term that assigns to a large number of heath care conditions related to heart. These medical conditions describe the unexpected health conditions that directly control the heart and all its parts. Medical data mining techniques like association rule mining, classification, clustering is implemented to analyze the different kinds of heart based problems. Classification is an important problem in data mining. Given a database contain collection of records, each with a single class label, a classifier performs a brief and clear definition for each class that can be used to classify successive records. A number of popular classifiers construct decision trees to generate class models. The heart disease database is clustered using the K-means clustering algorithm, which will remove the data applicable to heart attack from the database.
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
- C4.5
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 namefrom 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.
C4.5:
C4.5 algorithm builds decision trees from a set of training data using the concept of information entropy. C4.5 is also known as a statistical classifier.
- Check for base cases.
- For each element x, discover the normalized information gain from dividing on x.
- Let x_best be the element with the highest normalized information gain.
- Create a decision node that breaks on a best.
- Repeats on the sublists obtained by dividing on x_best, and add those nodes as children of node.
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.