Frontiers in Health Informatics
Volume:2 Issue: 2, 2013

Intelligent computer programs were implemented to aid physicians and other medical professionals in making difficulty medical decisions. In this paper, a computer aided detection (CAD) system is designed that classifies prostate neoplasia into two classes (cancer and hyperplasia) for supporting urologist. Early detection of Prostate Cancer (PCa) is crucial for patient survival. Automated diagnostic method could reduce diagnostic complications, financial and human resources. In presented system, data from 360 patients were used who diagnosed after biopsy with cancer and benign hyperplasia of prostate. In order to design present CAD, we have used; Bayesian regulation Backpropagation Neural networks (BRBNN). Performance of selected algorithm in detection of prostate cancer was evaluated by calculating sensitivity, specificity indicators and areas under receiver-operating characteristics (ROC) curve. The result of present study shows that, CAD based on BRBNN could serve as a strong diagnostic tool with 92.88% specificity, 90.62% sensitivity, 91.67% accuracy for early automatic detection of prostate cancer from benign prostate hyperplasia.

one of the major aspects of computer project management is accurate information about the amount of time, effort and cost required to accomplish project. This problem is possible if to have information about the project and its development team. In order to model such as CoComo have been introduced, that’s every project with a number of factors have been showed and using these factors to estimate the amount of effort required for the project. In this paper, by this factors and using data mining techniques with the help of decision trees a method for predicting the effort of software projects has been presented, that it is more accurate than similar method. The accuracy of this assessment criterion has been studied.

Today''s expansion and growing e-commerce through web sites and e-banking portals becom that identified the critical success factors in e-banking is deemed necessary. Several levels and indices to measure critical success factors in electronic banking, In this study, we use fuzzy logic. In this paper, The measurement and analysis of critical factors in the success of e-banking To evaluate them using fuzzy analytic hierarchy process. The results of this study can be help to bank managers and owners on proper strategies to overcome barriers and gain competitive advantage.

Data mining in healthcare is an emerging field of high importance for providing prognosis and a deeper understanding of medical data. Healthcare data mining attempts to solve real world health problems in diagnosis and treatment of diseases. One of the most important usage of data mining in machine learning domain is differential diagnosis. The differential diagnosis of erythemato-squamous diseases is a difficult problem in dermatology. The goal of this research is gaining to a high performance differential diagnosing of erythemato-squamous disease by using KNN Learning algorithms. UCI data base including 366 records has been used in this research. KNN algorithm was implemented and accuracy of system was compared with other methods used to diagnose such disease previously the highest accuracy was related to k=5. Sensitivity, specificity and accuracy in the best condition were respectively obtained 1, 1, and 0.98 and compared with other methods. KNN approach with preprocessing based on Relief-F strategy and Cross Fold modeling could be suggested as an effective data mining method to classify and diagnosis different diseases.

Nowadays medical sciences and physicians are faced with the volume of data. Since the diagnosis is not always easy, therefore the physician should consider results of the patient tests and decisions taken in the past for patients with similar conditions in order to make a good decision. In other words, the physician will need knowledge and experience. However, due to the large number of patients and any patient''s multiple tests, the need for an automated tool to explore the former patients is felt. One of the important methods used to derive data is data mining. The aim of this paper is the application of classification algorithms for the diagnosis of diabetes and breast cancer and identifying the best algorithm. By comparing the obtained results, it turns out that there is no algorithm with maximum efficiency.

Todays, we can use of new technology tools for restoring and saving data in huge size and so we need a new science for searching in these huge data source and finding useful and neccessory results in them. Data mining is a science that is searched automatically in huge data for finding models and association ruls in them where other statistical analysis can’t do that. The medical science is one of sciences that need to use of these tools for analyzing their huge data and creating predictive model with new computation ways. The purpose of this research is review of ruls and application area of predictive data mining in medical sciences and presenting a frame work for creating, evalution and explointion of data mining models in this. In this article we reviewed researches that are published recently about predictive datamining in medicine and we try to highlight important problems and summarizing data mining ways and algoriths that use in this area as a learning set. This research is shown that datamining preidiction provide neccessory tools for researcher and medical doctors to improvmet in prevention of disease, diagnosis ways and their treatment programs. And also it seems the way of data mining in medical are mostly clustering and classification.