An Accurate Prediction And Diagnostic Of A Disease Essay

Almost everyone knows somebody who is suffering from diabetes, from family members or just acquaintances; the number of people associated with diabetes is colossal. A total of 29.1 million people or 9.3% of the population have diabetes according to Centers for Disease Control and Prevention (CDC), (2014). This incredibly dreadful disease is commonly known as diabetes; its medical term is Diabetes Mellitus. “Mellitus is the Latin word for “sweet like honey”, referring to the excess glucose in the blood and urine of a patient with diabetes” according to Ladd, D. L., & Altshuler, A. (2013).

This is the study of the disease and implementation of machine learning for finding a solution for the diagnosis. Basically many intelligent agents are existing already. The task is not to define or develop a new agent rather it is just the research on several aspects of these agents and diabetes in our society.

Type 2 diabetes mellitus, a state of relative insulin deficiency with underlying insulin resistance, accounts for majority of the cases of hyperglycemia worldwide. An estimated 422 million people worldwide have diabetes [1], and this number is expected to reach 592 million by the year 2035 [2]. Almost 30 million Americans (9% of the population) have diabetes, with estimated total health care costs of $245 billion due to extensive complications, primarily micro- and macrovascular pathology.

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

The purpose of this paper is to provide a unique and concise solution to a biomedical problem from a supervised learning and statistical perspective. As the solution is purely from a statistical perspective without support from proper biomedical theories, the solution might not serve practical or meaningful biomedical values. In our solution, we have reduced feature dimension to as low as 0.1%. The selected features served as biomarkers might be useful to the diagnosis of LGL leukemia, but it requires further analysis and proof in the field of biomedical

The quality of healthcare diagnostics and treatment heavily relies on information about the patient’s condition. EHRs

Through research we can see the causes, misdiagnosis, treatment, types and symptoms of eating disorders. Pressures from society and dislike towards oneself seem to be the largest factor in the development of eating disorders. They can often mask other serious symptoms. Treatment can vary but can interplay with the treatments of mood disorders. The symptoms range between the different types of eating disorders. Diagnosing eating disorders can be difficult because most patients perform tell-tell signs when they are alone. There is also no well-defined meaning of binge. The importance of regular evaluation of patients about eating disorders is key in preventing and treating. Research is plentiful and wide ranging. Studies are

Mental health disorders has been very strenuous and complex to understand and diagnose, many diagnosis criteria such as DSM have been used to determine the disorders and medication (Szabo, 2011). I am asserting that “Yes, Psychopathology diagnostic classification systems (such as the DSM or ICD) are of little use to health professionals and patients because behaviours are often misidentified and medicalised”. To assert this, this paper is going to explore four main topics that contribute to the little use of psychopathology diagnostic classification system, these include downsides for using DSM diagnostic criteria, Medicalisation and misdiagnose, consequences of medicalisation to the health professionals and community and Psychological trauma in patients. Szabo, (2011) states that Diagnostic and Statistical Manual of Mental Disorders (DSM) has been utilised to diagnose and categorize mental illness in psychiatry. However this diagnosis is focused on definitions of operational rather than the cause (Warelow & Holmes, 2011). This paper will first explore the downside of using DSM criteria to diagnose mental health.

In this experiment, we found that the performance of random forests is optimal with around 70% accuracy on publicly available “Diabetes 130-US hospitals for year 1999-2008” dataset. The proposed model also indicates that information regarding diagnosis, age, race, medications, admission types, lab procedures are highly influential for readmission classification. In this work, the result is evaluated in different criteria. The best AUC and f-measure values obtained on the experiment dataset were 70.1% and 66.6% the classification of the readmitted patients from non-readmitted patients using random forests. It can be observed Random Forests is performed optimal than Naïve Bayes and C4.5.

Hosseinzadeh et al. In [17] this work analyzed predictability of hospital readmission without targeting any specific disease. In this analysis considered a dataset ,covers demographic, clinical procedure-related and diagnostic-related features along with drug information of patients predict the readmission within 30 days (short-term readmission prediction) above 65 years of age. Therefore, it’s contained comprehensive results of feature reduction methods and the performance of the prediction models is modest. Consequently, this studied did not consider cost analysis and grouped features importance mining and these studied not targets towards specific disease prefer like diabetes.

Every 5 minutes 2 people die and 14 more adults are diagnosed with diabetes. Because of this there has been and continues to be a lot of research into diabetes and how best to control and detect it. Data analytics is key to this research due to its positivism and pragmatic approaches. With the incidence of diabetes growing each year, (Onkamo et al. (1999)) detection and control will become more important. The aim of this paper is discuss the methods and some of the research already conducted to try improve on this research or discover new research areas in diabetes control and detection. Many aspects of Diabetes control are all about the individual and can’t be applied globally eg, exercise diet.

To arrive at an accurate diagnosis of the disease is a hideous task both for the doctor as well as family members. Patient should be accompanied with a reliable person while visiting doctor. There are various stages of Parkinson's disease and its severity of symptoms vary from patient to patient.

This study utilized the Worchester Heart Attack Study data and R Studio software to predict the mortality factors for heart attack patients. The medical data include physiological measurements about heart attack patients, which serve as the independent variables, such as the heart rate, blood pressure, atria fibrillation, body mass index, cardiovascular history, and other medical signs. This study employed the techniques of supervised learning and unsupervised learning algorithms, using classification decision trees and k-means clustering, respectively. In addition to performing initial descriptive statistics to estimate the general range of critical factors correlated with heart attack patients, R Studio was used to determine the weight of each of the significant factors on the prediction in order to quantify its influence on the death of heart attack patients. Furthermore, the software was used to evaluate the accuracy of the predicted model to estimate death of heart attack patients by using a confusion matrix to compare predictions with actual data. Finally, this study reflected on the effectiveness of the data mining software conclusions, compared supervised learning and unsupervised learning, and conjectured improvements for future data mining investigations.

DuPont describes itself as, “a science company with an extraordinary range of materials science, chemistry, biological science and engineering capabilities, and a corresponding breadth of products.” (DuPont, 2016). The global company is made up of three different pillars of innovation including food, energy, and protection. These main pillars cover a wide range of services and products that DuPont provides its customers. Scientific breakthroughs and advancements allow DuPont to constantly expand its capabilities and products. Therefore, new products are often created.

Data Mining is the non-trivial extraction of potentially useful information about data. In other words, Data Mining extracts the knowledge or interesting information from large set of structured data that are from different sources. There are various research domains in data mining specifically text mining, web mining, image mining, sequence mining, process mining, graph mining, etc. Data mining applications are used in a range of areas such as it is used for financial data analysis, retail and telecommunication industries, banking, health care and medicine. In health care, the data mining is mainly used for disease prediction. In data mining, there are several techniques have been developed and used for predicting the diseases that includes data preprocessing, classification, clustering, association rules and sequential patterns. This paper analyses the performance of two classification techniques such as Bayesian and Lazy classifiers for hepatitis dataset. In Bayesian classifier there are two algorithms namely BayesNet and NaiveBayes. In Lazy classifier we have two algorithms namely IBK and KStar. Comparative analysis is done by using the WEKA tool.It is open source software which consists of the collection of machine learning algorithms for data mining tasks.