Association Rule Mining Model : Study Student Achievements

The argument of the author consists of the limitations of the practical model of diagnosis, which have current systems based on the AI. Salvado underlines, even throughout the significant gap between the amount of information of which the computer may know, compared to the human brain, there is still a great difference between the accuracy of diagnosis provided by a real doctor and a program. The examples provided by the author brightly support his argument and show disadvantages and gaps in the current model of artificial intelligence technology application.

This algorithm was simulated with Matlab. These datasets and the mentioned characteristics are considered and the algorithm of each dataset with different slopes for the activation fumcion of interest were evaluated so that the best slope can be obtained. After running the program for several times and computing the average to obtain the best result, the optimum slope was evaluated for each dataset and the best slopes for Breast Cancer, Diabetes, Bupa, and

Health care information systems cover a wide range of issues dealing with the supervision and use of biomedical information. Health information systems are central support tools in the administration of health care services. An acceptable health information system is important not only for evaluating the health needs of populations but also for preparation and for application of health interventions. It is equally imperative in the assessment of programs. The goal of information systems is to create an appropriate working environment. This is done by providing initial and going training, allocating resources, and by managing unintended consequences (Wager, Lee & Glaser, 2009).

These measurements include the assessment of risk factors[61], quality of care[62], diagnostic criteria[63], etc. Most of these studies used rule-based method[62, 63] to detect clearly defined and less complex (fewer expression variations) measurements, such as glucose level and body mass index. For some ambiguous and complex measurements, such as coronary artery disease and obesity status, machine learning plus external terminologies[61] are often

For this component, we plan to optimize the DSS in three ways: 1) by enhancing the knowledge representation of the patient database; 2) by adding a probabilistic component to the classification system; 3) by improving the prediction accuracy of the classification system through the creation of statistically coherent committees. We propose to revisit the machine learning choices made in our preliminary study and integrate into the classification process descriptive features represented in different formats. We hypothesize that the information they carry may be important to consider in conjunction with the information carried by other features. This is particularly important given the sensitivity of the new task that we are planning to study.

Logistic regression is used in this study to compare the non-linear neural network approach to come up with the best influenza vaccination model to be used for prediction purpose in the medical practice of primary health care physicians; where the vaccine is normally dispensed. In this study, logistic regression has been used widely to analyze

Referring to a study conducted by IBM, over $30 billion are spent in US each year on unnecessary hospital admissions. Clinical and behaviour data can be used to identify patients at high-risk and lead healthcare providers to develop strategies to care for patients before it is too late. The prediction of patient’s problems can reducing the number of unnecessary hospitalizations and the related costs.

The machine-learning procedure was faster and provided greater accuracy in predicting death risk in people with serious heart disorder than the existing methods.

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.

The area I am concentrating is the feature extraction and Feature selection of clinical image.

Health care facilities, physicians, health care personnel and most importantly patients will definitely benefit from the data mining health care information. This paper will discuss different ways data mining health care information will be beneficial to health care facilities, health care personnel and patients alike and also the risk of data mining health care data.

Clinical decision-support systems (CDSS) apply best-known medical knowledge to patient data for the purpose of generating case-specific decision-support advice. CDSS forms the cornerstone of health informatics research and practice. It is an embedded concept in almost all major clinical information systems and plays an instrumental role in helping health care achieve its ultimate goal: providing high quality patient care while, at the same time, assuring patient safety and reducing costs. This computer based systems designed to impact clinician decision making about individual patients at the point in time that these decisions are made. If used properly, CDSS have the potential to change the way medicine has been taught and

This research aims to present a hybrid intelligence model that can analyze medical data and processes, and provide decision support for

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.

Machines with AI could be used in medical facilities to diagnose illnesses or to search for symptoms or the presence of anomalies in the bodies of patients (Keiser, 2017). Doctors constantly lament the delays in diagnosing diseases in a developing country. The use Artificial