Abstract - Road Traffic Accident Is Very Serious Matter

To begin with, an accident will not be solved, if we respect the citizen's privacy too much. When there is an accident, people are asked by the police about it. But, if the privacy is protected too much, then the people will not tell the truth. In conclusion, the accident will not be solved.

Accurate data mining works wonders for medical billing agencies to improve their level of service and collection. With new and improved innovations being incorporated in medical billing software, unique reports can be executed that help in providing specific information which then assists in decision making and implementing improvements.

Many people have lost their lives on the road and other were severely injured. Over a million of people across the globe dies commuting from home, work or school. This is devastating to hear each day there are thousands of fatal car crashes. There are several contributing factors to collisions such as; speeding, driving under the influence, distracted while driving. For example, the person search for a radio station to listen to while driving. This causes the driver to take their eyes off the road; within seconds a collision can occur. Especially if the car ahead of the driver suddenly stops because the traffic speed decreases.

The ability to predict patient’s condition is very important in hospital settings. Patients’ condition is based upon diverse factors like personal, psychological and other health problems. Different data mining algorithms are used to achieve the goal of predicting the patient’s condition.

Logistic regression model, as a usual approach before, was used to analyze the stroke outcomes' data. Fortunately, because of its potentially more powerful high-level prediction performance, machine learning algorithms have been proposed as an alternative to analyze large-scale multivariate data. Support vector machine (SVM) is one of the most popular machine learning methods to use for recognition or classification. Support vector machine (SVM) is one of the most popular machine learning methods used for recognition or classification.

Data mining for healthcare is useful in evaluating the effectiveness of medical treatments and it is interdisciplinary field of study that has its roots in databases statistics machine learning and data visualization. Diabetic disease refers to the heart disease that develops in persons with diabetes. The term diabetes is a chronic disease that occurs either when the pancreas does not produce enough insulin. The cardiovascular disease is class of diseases that involves the heart or blood vessels Even though many data mining classification techniques exist for the prediction of heart disease there is insufficient data for the prediction of heart diseases in a diabetic individual. The main objective focus on this research is to find an optimal

This is because of the fact that the driver is not able to control his vehicle when he is asleep and by the time he realizes it, there is an accident. The vehicle is at a very high speed on highways due to which handling is tough and getting the vehicle to halt in such a condition is difficult. Due to this many automobile companies are trying to research onto how an accident which occurs due to driver fatigue can be prevented. In this project we will generate a model which can prevent such an incident. The Purpose of such a model is to advance a system to detect fatigue symptoms in drivers and control the speed of vehicle to avoid accidents. The Purpose of such a model is to advance a system to detect fatigue symptoms in drivers and control

Even though this country is on the lower end of death rate due to traffic accidents in the world, it is still too high for anyone’s liking. I’m here to propose to you some methods to lower the annual traffic fatalities.

Abstract - The healthcare industry collects large amounts of Healthcare data, but unfortunately not all the data are mined which is required for discovering hidden patterns and effective decision making. We propose efficient genetic algorithm with the back propagation technique approach for heart disease predic-tion. This paper has analyzed prediction systems for Heart dis-ease using more number of input attributes. The System uses medical terms such as Gender, blood pressure, cholesterol like13 attributes to predict the likelihood of patient getting a Heart dis-ease.

Many hospitals are maintaining their patient’s database online like the records related to tests suggested, their results and the prescriptions suggested. This generates huge data which could be in any form like text, numbers as well as images and videos. In fact, all this data is important in making clinical decisions. In order to handle such a large data efficiently, usage of multistage classifier has become necessary. In existing systems, all the features are tested at a time by the classifier in order to detect whether patient is suffering from that particular disease or not. The entire testing consumes lot of time if the system is testing all attributes of a patient who is not actually suffering from the disease. So in such cases if we test the attributes step by step with few attributes in each step then we will be able to arrive at conclusion in primary stage itself leading to more efficient use of time as well as money. Simultaneously patient is also relieved from unnecessary stress as well as fatigue. This system optimizes resources very efficiently. Also it aims to identify the problem in the very preliminary stage and suggest reliable solution to problems increasing life of

From the given data and the decision tree we have inferred the various costs that the insurance company would have to pay for under different circumstances.

In conclusion, as discussed above, the causes of a car accident are fatal because of the large force encounter when the car hits but at the same time can be controlled by some effort. The car accidents have many sources not only speeding, lack of attention or uncontrollable factors because there are many other causes of car accidents. The safety of the car would be more advance in the later time, with much more protective equipment preventing from shock in the human to sensor that automatically pre-calculate the upcoming casualties.

Accidents happen every day, which consists of terrifying situations in an individual’s life and there are hundreds or thousands of accidents involving automobiles. A known fact is that most number of people has no survivals as they do not get to hospital in time after their car accident. The 2010 national Automobile census, mentioned that more than 90% of reported accidents were caused by driver and less than 10% was due to outside or car problems. As the advances of technology increases, so does the danger to people. This is due to there being production of faster cars that are more powerful, therefore increasing the chances of danger.

Car accidents can happen to drivers anytime, anywhere. "According to the National Safety Council, which stated that more than 2.5 million collisions back every year, making it the most common type of car accidents, it is also known that the accident rear end as incidents of injury, because the nature of the collision leads often in whiplash injury the driver in the car in front and about 20% of people who participated in a rear collision injury symptoms of this kind. ", (NHTSA, auto-accident-resource.com). Among the car accidents, the teenage group is the only age group who is number of deaths is increasing instead of decreasing. Also, all the people are exposed to risk and actually every one of them has got car

The ultimate goal of this study was to compare Poisson and Negative binomial regression models using road accident (count) data to assess which of them significantly fit count data. The data for this study were obtained from the Building and Road Research Institute of the Council for Scientific and Industrial Research (CSIR) on the number of people killed by road accidents in Ghana, including days that those accidents occurred within the period from 2001-2010.The maximum likelihood estimation was employed to determine the parameter estimates for each model with R statistical package. The results showed that the negative binomial regression model best fits road accidents’ data significantly as compared with the poison regression model. The results also revealed that the day an accident occurred within the week significantly affected the expected number of people killed in that accident. It was identified that Saturday significantly had the highest number of people killed in road accidents in Ghana.