Predicting The Severity Range Of The Diabetes Patients Using Data Mining Technique

ABSTRACT:

Diabetes is a chronic disease caused due to the expanded level of sugar addiction in the blood. Various automated information systems were outlined utilizing various classifiers for anticipate and diagnose the diabetes. Data mining approach helps to diagnose patient’s diseases. Diabetes Mellitus is a chronic disease to affect various organs of the human body. Early prediction can save human life and can take control over the diseases. Selecting legitimate classifiers clearly expands the correctness and adeptness of the system. Due to its continuously increasing rate, more and more families are unfair by diabetes mellitus. Most diabetics know little about their risk factor they face prior to diagnosis. This paper explores the early prediction of diabetes using data mining techniques. Many algorithms are developed for prediction of diabetes and accuracy estimation but there is no such algorithm which will provide severity in terms of ratio interpreted as impact of diabetes on different organs of human body.

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 Diabetes detection using data mining technique

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 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.

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Predicting The Severity Range Of The Diabetes Patients Using Data Mining Technique