ABSTRAT:
The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not “mined” to discover hidden information for effective decision making. Data mining has been a current trend for attaining diagnostic results. Huge amount of unmined data is collected by the healthcare industry in order to discover hidden information for effective diagnosis and decision making. Data mining is the process of extracting hidden information from massive dataset, categorizing valid and unique patterns in data. Researchers all over the world are working in either multi agents or in ontologies for developing system in health care domain. It might have happened so many times that you or someone need doctor help but they are not available due to some reason. The health management system is an end user support and online consultation project. Here we propose a system that allows users to get guidance on their health issues through an intelligent health care online system. The objective of our paper is to predict Chronic Kidney Disease (CKD), Heart Disease and Liver Disease using clustering technique, K-means algorithm
EXISTING SYSTEM :
The rate of kidney, liver and heart diseases is increasing at an exponential rate. The busy lifestyle of people in this era with all the fast food in the lunch break and getting back to sitting and working has pushed as over the edge. Along with this people today have a lack of exercise and are less active. For most of them recreation is just another movie in bed or anything technology based. Physical activities have reduced drastically. These factors boosted the rate of kidney, liver and heart diseases to an unfortunately high percentage. In a developing country like ours the rate of heart diseases has the same effect. The annual mortality rate per 100,000 people from cardiovascular diseases in Bangladesh has increased by 128.9% since 1990, an average of 5.6% a year. Prediction of heart diseases is a difficult and risky task. Since it is directly dependent on people’s’ health, accuracy is a major factor. If not predicted accurately it can be disastrous. This research therefore focuses on the comparison of different data mining techniques to predict it
EXISTING SYSTEM DISADVANTAGES:
1.LESS ACCURACY
2. LOW EFFICIENCY
PROPOSED SYSTEM :
the Liver Disease Diagnosis Based on Neural Networks ”with methodsC4.5, CART, Naïve Bayes, SVM in 2015. SVM is the optimal model as it gives more accurate result than other algorithms [15]. Baitharua et. al. have analyzed the Data Mining Techniques For Healthcare Decision Support System Using Liver Disorder Dataset with methodsJ.48, SVM, Random Forest in 2016. 45 By analysing the results Random Forest gives the overall best classification result [16]. Gulia et. al have proposed the Liver Patient Classification Using Intelligent Techniques ”with methods SVM, Random Forest in 2014. The results indicate that Random Forest algorithm outperformed all other techniques with the help of feature selection
PROPOSED SYSTEM ADVANTAGES:
1.HIGH ACCURACY
2.HIGH EFFICIENCY
SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10
HARDWARE REQUIREMENTS:
Processor : Core I3
RAM Capacity : 2 GB
Hard Disk : 250 GB
Monitor : 15″ Color
Mouse : 2 or 3 Button Mouse
Key Board : Windows 08/10
