The Heart Disease Prediction System Computer Science Essay

The Heart Disease Prediction System Computer Science Essay There are enormous amount of data available from medical industry which could be useful for medical practitioners when it is used for discovering hidden pattern with help of existing data mining techniques. The basic medical records from a patients profile can be useful in identifying hidden pattern with data mining techniques. In this paper, NaÃÆ'Â ¯ve Bayes algorithm to predict heart disease is implemented with basic records of patients like age, sex, heart rate, blood pressure etc., from a sample dataset. The benefits, limitations, and technical details of this implementation will also be discussed in this paper. 1 Introduction Over these years in medical history, many types of medical problems have been identified and many data are available regarding a particular problem. But not all the medical data are same, but there are many patterns hidden inside those data which needs to be identified. Data mining techniques could help identify these hidden patterns by knowledge discovery. In the medical field, patients health issues are predicted by doctors intuition or experience [2] where the knowledge rich data is suppressed which results in high medical expenses and unnecessary medical tests. In recent years, there are many researches being conducted in order to find the hidden pattern from basic medical data [1]. Identifying these hidden pattern would result in a developing an efficient decision making system in medical industry which aide as a tool to support doctors decision making or at least serve as a prediction system for any medical issues. In this paper, we have taken into consideration of heart disease and predict it using the set of data that are already in existence with the help of data mining technique. The algorithm that we have chosen is the NaÃÆ'Â ¯ve Bayes algorithm, this algorithm is ideal for a vast amount of database that may contain hundreds and thousands of rows and columns. The NaÃÆ'Â ¯ve Bayes algorithm provides the intended output faster and more accurate as the number of data in the database increase. 1.1 Problem Scenario There are only few decision support systems available in medical industry whose functionalities are very limited. As mentioned earlier, medical decisions are made with doctors intuition and not from the rich data from the medical database. Wrong treatment due to misdiagnosis causes serious threat in medical field. In order to solve these issues data mining solution was with help of medical databases was introduced. 1.2 Related Work There are many techniques available to discover knowledge from medical database [1]. Researchers at Southern California used data mining technique to discover the success and failure of back surgery in order to improve medical treatment [3]. Shouman et al [4] implemented predictive data mining to diagnose heart disease of patients. Palaniappan et al [2] developed a prototype Intelligent Heart Disease Prediction System (IHDPS), using data mining techniques. 1.3 Objective In this paper, NaÃÆ'Â ¯ve Bayes algorithm to predict heart disease is implemented with basic records of patients like age, sex, heart rate, blood pressure etc., from a sample dataset. Based on the literature survey NaÃÆ'Â ¯ve Bayes algorithm was found to be an effective technique. The probabilistic method helped in finding the converse probability of the conditional relationship. The dependence relation may exist between two attributes of data set which can be determined with this algorithm. 2 Data Preparation In order to implement the algorithm, a medical data was required. The sample dataset used for the purpose of implementation of algorithm was obtained from Cleveland Clinic Foundation. The sample of dataset is shown in the below figure (Figure1.) C:UsersMadan KumarDesktopUntitled2.jpg Figure1. Sample dataset 2.1 Dataset Source The Cleveland institute medical data was downloaded from website of University of California, Irvine. 2.2 Dataset Attributes The dataset consists of 16 attributes. The last attribute of dataset consists of value 0 and 1. The value 0 indicates that the patient does not have heart disease whereas 1 indicates that the patient has a heart disease. The prediction of algorithm can be verified with this value while evaluating the algorithm. The first 15 attributes are shown in the figure2. C:UsersMadan KumarDesktopattri.jpg Figure2. Dataset attributes 3 Program Architecture The program was implemented using JAVA. Apache TOMCAT server and MySQL Database is also used. The NaÃÆ'Â ¯ve Bayes algorithm has three class files: Calculation.java, Prediction.java, and Detection.java. Detection.java reads the data file from the source path and stores the attributes into temporary array list. The mean and standard deviation values calculations are performed and probability calculation is also done in Prediction.java. All the dataset attributes are defined in calculation.java where mean and standard deviation of attributes were calculated. The calculation.java calls the other two classes while executing the program. Figure3 represents the program architecture. C:UsersKirubanidhyDesktopArchitecture.jpg Figure3. Architecture 3.1 Building and running a Demo TOMCAT server is used to present the output in web based form. The output will run in localhost. The MySQL database is used to identify the patient records. At the execution point, the local host is accessed and 15 questions will be displayed which will be obtained from user and algorithm will be called to calculate and predict the disease possibility on that person. A report will be generated at the end of the demo which says if the person is predicted with heart disease or not. In general, 1. Obtains the values from user. 2. Reads the data file. 3. Calls the algorithm and calculates mean, deviation, and probability of attributes. 4. Generates a report displaying the values given with the prediction of disease. 4. Implementation All the attributes of dataset is of a numerical value that has some meaning. The meaning of dataset attributes are as shown in figure2. Example: the attributes sex is denoted with values 1 and 0 where 1 denotes Male and 0 denote Female. Fasting blood sugar values are also denoted using 1 and 0 where 1 denotes >120mg of fasting blood sugar level and 0 denotes These values from the data file are accessed by the NaÃÆ'Â ¯ve Bayes algorithm. The values 0 and 1 are extracted from data file and stored to an array list for each attribute e.g. age array list, sex array list, and chest pain type array list etc., in order to perform calculation. Here, the values are defined on what those values stands for before storing to the array list. The sample of the interface (for obtaining slope value) is shown in figure4. Here the un-sloping, flat, and down- sloping represents the value 1, 2, and 3 respectively. C:UsersMadan KumarDownloadsUntitled.jpg Figure 4. Interface Sample C:UsersMadan KumarDownloadsUntitled2.jpg Figure5. Sample of report format 5. Modules Description Analyzing the Data set The attribute Diagnosis was identified as the predictable attribute with value 1 for patients with heart disease and value 0 for patients with no heart disease. The attribute PatientID was used as the key; the rest are input attributes. It is assumed that problems such as missing data, inconsistent data, and duplicate data have all been resolved. Naives Bayes Implementation in Mining Bayes Theorem finds the probability of an event occurring given the probability of another event that has already occurred. If B represents the dependent event and A represents the prior event, Bayes theorem can be stated as follows. 5.2.1 Bayes Theorem Prob (B given A) = Prob (A and B)/Prob (A) To calculate the probability of B given A, the algorithm counts the number of cases where A and B occur together and divide it by the cases where A occurs alone. Applying NaÃÆ'Â ¯ve Bayes to data with numerical attributes, predict the class using NaÃÆ'Â ¯ve Bayes classification: Figure6 (a) Top Mean (b) Bottom Standard Deviation Figure6 (c) Laplace Transform 6. Evaluation User enters the values for the questionnaire to find out whether the patient has a heart disease or not. By feeding sample data from the dataset and performing the mining operations with the NaÃÆ'Â ¯ve Bayes algorithm, it is found out that the NaÃÆ'Â ¯ve Bayes algorithm gives 95% probability in predicting if patient have heart disease or not. 95% accuracy is quite good to use as a decision support system. The figure shows the accuracy of NaÃÆ'Â ¯ve Bayes algorithm (figure7). The figure shows the highest probability of correct predictions and lowest probability of incorrect predictions. C:UsersMadan KumarDesktopUntitled1.jpg Figure7. Model Results of three algorithms [2] 7. Limitations Apart from the benefits like probabilistic approaches and fast reliable algorithm of NaÃÆ'Â ¯ve Bayes, the serious shortcoming of the algorithm is its ability in handling small datasets. NaÃÆ'Â ¯ve Bayes classifier requires relatively large dataset to obtain best results. Yet, studies showed that Naive Bayes algorithm outperforms other algorithms in accuracy and efficiency. Notable limitation of this paper is the usage of small dataset. This dataset can be used for training or testing purpose only. Also the dataset could include more attributes for a more effective prediction in supporting clinical decisions. 8. Future Work The algorithm is working well with this sample dataset. Implementing the algorithm with large dataset could give better results which can aid as a supporting tool in making medical decisions. In future, other possible algorithms could be implemented where efficiency of all algorithms could be analyzed to decide on best suitable technique in terms of speed, reliability, and accuracy. 9. Conclusion In this paper, NaÃÆ'Â ¯ve Bayes algorithm is the only algorithm used for calculation of attributes and prediction. Efficiency and accuracy of the algorithm in predicting were discussed. Designing effective models are constrained by size of the datasets and noisy, incorrect, missing data values. The prototype developed so far has been generally tested by computer experts and not by the doctors. For effective understanding of the health issues, medical experts have to work collaboratively and test the prototypes in order to implement the system in real life to support medical experts in taking clinical decisions.

Applying the Background and Methodology of the Research Process Essay

Introduction I have chosen to analyze the research and study on Childhood Obesity: Can electronic medical records (EMRs), customized with clinical practice guidelines improve screening and diagnosis. The project was done to determine if customization would affect the outcome of prevention, screening, and treatment and improve the rate of diagnosis of obesity in children 7-18 years of age. Statement of the Problem The failure to achieve a decrease the child obesity in our nation that was outlined in 2010 by the U.S. Department of Health and Human Services, they have recently released the 2020 projections and objectives that will intensify the focus on primary care physicians and state agencies to attain this goal. Primary care practices are a profound part of identifying, preventing, and managing childhood obesity. Clinicians are being urged to record BMI’s on all patients, in cases of identifying obesity/overweight individuals they would provide educational instructions, counsel patients on nutrition, and weight maintenance. Practitioners rarely record accurate BMI percentages for pediatric patients, instead they rely on physical appearance or regarded as a result of some other specified cause. This is important to health care because of the subsequent medical conditions such as; type II diabetes mellitus, hyperlipidemia, hypertension, sleep apnea, and orthopedic problems. Providers have stated that the barriers of diagnosing, and managing childhood obesity is lack of practice resources, time, reimbursement, family motivation, and family resources. Purpose of Study Childhood obesity and overweight is a priority health issue, in the United States 32% of children 2-19 being overweight and 18.7% age 6-19 being obese (Ogden, Carroll, Curtin, Lamb, & Flegal, 2010). The development of diseases due to obesity is rising as obesity in our children becomes more profound. Children who had percentiles of BMI in the index between the 95th and 98th became obese adults, a percentile higher than the 98th percentile was related to adult obesity 100% of the time In this study there was a retrospective review done in regards to prevention, screening, and diagnosis of obesity in children. Data was collected and compared for BMI documentation. The purpose of the study was to determine whether EMR customization using evidence based practices introduced by the National Association of Pediatric Nurse Practitioners and Expert Panel guidelines for prevention of obesity would improve the rate of the diagnosis of childhood obesity (Savinon, Taylor, Mitchell, & Siegfried, 2012). The Design A quasi-experimental design was used comparing outcomes of a group with written records from September 1, 2009 through December 31, 2009 to those using EMR September1,2010 through December 31, 2010 Hypothesis In this study the hypothesis is based on a conceptual model. The use in the study of growth charts, scoring risk questionnaires, BMI documentation, diagnosis of overweight or obesity in each study individual. This data was able to provide guidelines with the ability to decrease the rate of obesity/overweight in children 2-19 if followed consistently. Evidence-Based Practice Guidelines The Health Eating and Activity Together (HEAT) clinical practice guideline developed by the National Association of Pediatric Nurse Practitioners (NAPNAP), and the Expert Panel recommendations were designed to provide practitioners with the most recent evidence based information to attack childhood obesity. Training of providers in the practice guidelines showed an improvement in confidence, ease, and frequency of obesity-related counseling, a structured training with tools for successful intervention. The study confirmed that the training with in office tools showed improvement in documentation and adherence to guidelines but not with just  training alone. There was a profound improvement seen after 3 and 6 month intervals in documentation of BMI percentages. Exposure to the guidelines through structured training and in office tools proved that provider practices in assessment and management regarding overweight and obese patients was greatly improved. Data Collection There were several variables abstracted from the written records and EMR using a chart audit form: race, religion, ethnicity, gender, age, provider type, payer source, height, weight, and BMI, Blood pressure, screening tests for lipids, and diabetes, diagnosis for overweight or obese. Demographics Statistically there were no significant variables differences in the demographics for each group. Race, gender, insurance status, and age were similar in both the written and electronic records. A larger amount of children with written records were African-American (53%) and male (58%). Implications for Practices Customizing EMR with clinical practice guidelines improved the use of recommendations for screening and identifying childhood obesity. Increasing people’s awareness and diagnosis will ultimately lead to better intervention and improved outcomes. Conclusion There were clear signs of increase in recording of BMI, completion of grow charts, growth charts, scoring questionnaires. Providers are trained and provided with in-office tools to make sure everyone is complying with the guidelines. The number of children diagnosed overweight or obese increased with electronic medical records. Increasing recognition and diagnosis will lead to a profound reduction in the rate of obesity in the future. It will also lead to improved interventions and improved outcomes for childhood obesity. Reference Authors; Savinon C. , DNP, FNP-BC’(Asst. Professor), Taylor-Smith J. PhD, RN, WHNP-BC, Canty-Mitchell J. PhD, RN (Professor), Blood-Siegfried, DNSc, CPNP (Associate Professor), (2012) 2012 Childhood Obesity: Can Electronic Medical

The Fight Against Alexander the Great Essay Topics

The Fight Against Alexander the Great Essay Topics Ok, I Think I Understand Alexander the Great Essay Topics, Now Tell Me About Alexander the Great Essay Topics! Employees are anticipated to champion distinctive courses of action. Students ought to be encouraged to include both textual info and their very own connections and implications. American teenagers essay is an extensive subject that can be focused on several different elements which affect the teens, their issues and respective areas in which teenagers have gained recognition. 8th graders are only learning how to come across themes in literature. Even if students take a specific course since they're really interested in the topic, this still doesn't signify they enjoy every facet of it. The teacher might need to provide models or instruction on developing a bibliography or works cited. The wonderful leaders can claim to observe the great. As a consequence the enormous country disintegrated after his death. He already defeated the most effective army on earth, lead by Darius. Callisthenes was a friend in addition to a guest nephew. The Persians were controlling the majority of the known world then, including Egypt. If you wished to join army, it would be critical. The mysterious intention of that murder puzzled ancient and contemporary historians for several years. This morning, both troops met on the battleground. The Alexander the Great Essay Topics Game His plan worked with good success. In exactly the same purchase form, you will give your name, email, and contact form. After you have the topic, answer the question and after that support your answer with three or more explanations for why you believe it. If you're looking for a quality-oriented company, we are the ideal company for you. A member of the group might have to come up and select the article for the remainder. Typically, it takes as many as 2 hours of time to comb through dozens of sites until you discover something exciting to write about. An animal or an innovation is a simple place to get started. Two commonly utilised within this place. Whether Alexander had plans for a world empire cannot be determined. In addition, even though the legend in document D where Alexander threw down the helmet of water, may be regarded as inspirational, it might also be regarded as disrespectful. Alexander's very first task was supposed to spread the Greek methods to the Persians. So as to ascertain the size of Alexander's cruelty you have to analyze a range of crucial events which occurred during his kingship. In general, regardless of the simple fact that sometimes Alexander the Great isn't called great, there are various reasons to consider him to be great. His principal Concern was supposed to continue to keep his Empire Functioning4. There are a few great topics to take into account when picking a topic for your argumentative essay. You ought to be proficient in the topic, have an overall idea about the chosen issue and can get the best arguments to demonstrate your thesis. The essay isn't the simplest task to master. Naturally there's much more to the essay than only the opening but a terrific essay is going to have amazing opening. Because of all of these vital jobs that will need to get mastered, attempt to select easy to research and intriguing topics for 8th grade students. Assessment and Reflection The teacher utilizes the LDC rubric to evaluate the students' writing and offer feedback to help students boost their performance. Students may offer feedback to one another on their opening paragraphs. When they finish reading this article, they will be able to take a small quiz to check their understanding. Dependent on the topic one ought to compile data to create a considerable base and argument in the essay. I ask that if they finish the post to develop a fast reaction to what they have learned thus far. We can aid you within this post! To get started writing your assignment you would want to encounter an interesting and promising topic.

Movies Based on Dean Koontz Books

Dean Koontz is one of the most prolific suspense writers alive. It is no surprise, then, that many of Koontzs books have been adapted into movies. Here is a complete list of Dean Koontz movies by year. Dean Koontz Film Adaptations 1977 - The Passengers aka The Intruder (1979 video release) This was adapted from the novel Shattered, which Koontz wrote under the name of K.R. Dwyer. It was filmed in France and Italy and released in French. The original title was Les Passagers, and it was also released on video in the US as The Intruder.1977 - Demon Seed  Based on the novel of the same name, it starred Julie Christie and Fritz Weaver as a couple whose super-computer Proteus IV gets a little too familiar with them.1988 - Watchers Based on the novel, boy (Corey Haim) meets dog. Dog is a super-intelligent runaway from a genetic research lab.1990 - Whispers Based on the novel, Victoria Tennant gets stalked in Canada. The tagline was, Fear shouts. Terror whispers.1990 - Watchers II Still based on the novel, the dog saga continues, now with Marc Singer and Tracy Scoggins.1990 - The Face of Fear   This was a TV movie based on the novel. It starred Pam Dawber and Lee Horsley. A killer stalks a guy who has psychic powe rs and is about to uncover his serial killer ways. Good thing he was a former mountaineer. The tagline was, Their lives are hanging by a thread, forty stories above the street. And a madman is trying to shoot them down.  1991 - The Servants of Twilight Based on the novel, Bruce Greenwood tries to protect a boy who might be the Antichrist.1994 - Watchers III  We cant get enough of that dog. This stars Wings Hauser.1995 - Hideaway   Based on the novel, Jeff Goldblum is brought back to life after a traffic accident, but now he has a psychic connection with a mad killer who is after his daughter, played by Alicia Silverstone.1997 - Intensity Based on the novel, in this TV movie, Molly Parker tangles with serial killer/kidnapper John C. McGinley.1998 - Mr. Murder  Based on the novel, this TV movie stars Stephen Baldwin as a mystery novel writer who gets cloned, and the clone is murder-y.1998 - Phantoms Based on the novel, the town of Snowfield, Colorado is not where you want to b e. Starring Peter OToole and Rose McGowan.1998 - Watchers Reborn aka Watchers 4  The dog keeps going, this time with Mark Hamill as a detective.  2000 - Sole Survivor  Based on the novel, this was a four-hour TV mini-series. Billy Zane grieves over losing his wife and daughter in a plane crash, but the sole survivor (Gloria Reuben) may know it was actually a nefarious plot.2001 - Black River Based on the novella, bad things are happening in this town.2013 - Odd Thomas Based on the novel, Anton Yelchin portrayed a fry cook who sees dead people.