Heart Disease Prediction using K Nearest Neighbour and K Means Clustering

ABSTRACT:

The widespread application of data mining is highly noticeable fields like e-business, marketing and retail has led to its application in other industries and healthcare sectors. The healthcare environs are still information rich but that has poor knowledgeable data. Techniques in Data mining have been commonly used to extract knowledgeable information from medical data bases Today medical field have come a long way to treat patients with various kind of diseases. Among the most menacing one is the Heart disease which cannot be detected with a stripped eye and comes suddenly when its boundaries are reached. Bad medical decisions would cause death of a patient which cannot be afforded by any hospital. To achieve a correct and cost effective treatment computer-based and support Systems can be developed to make good decision. Many hospitals use hospital information systems to manage their healthcare or patient data. These systems produce huge amounts of data in the form of images, text, charts and numbers. K nearest neighbor and K means used to support the medical decision making efficiently.

PROJECT OUTPUT VIDEO:

EXISTING SYSTEM:

• In exiting approach syndicates K Nearest Neighbor and genetic algorithm to expand the classification accurateness of heart disease data set. They used genetic search as a heavens measure to crop redundant and immaterial attributes and to rank the attributes which contribute more towards classification. Least graded attributes are detached and classification algorithm is built based on estimated attributes. This classifier is accomplished to categorize heart disease data set as either healthy or sick. In exiting paper recommended for only classification not a prediction so some safekeeping issues is accrued. In existing system Old genetic algorithm are used, so there is no prediction it leads to the low security of the system.

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

DISADVANTAGES OF EXISTING SYSTEM:

• 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:

• In exiting system only proposed for classification technique. In this paper proposed classification and prediction of K Nearest Neighbor with K Means classification. This combined approach of K Nearest Neighbor and K-Means clustering to improve the classification accuracy of heart disease data set and the prediction can be used to provide the security in heart disease medical data.

ADVANTAGES OF PROPOSED SYSTEM:

• 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.

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

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

• System           : Pentium Dual Core.

• Hard Disk      : 120 GB.

• Monitor          : 15’’ LED

• Input Devices : Keyboard, Mouse

• Ram                : 1GB.

SOFTWARE REQUIREMENTS:

• Operating system : Windows 7.

• Coding Language : MATLAB.

• Tool                       : MATLAB 2018