OBJECTIVES: The interpretation of clinical data for the diagnosis of coronary heart disease can be done using algorithms in data mining. Most clinical data interpretation systems for diagnosis developed using data mining algorithms with a black-box approach cannot recognize examination attribute relationships with the incidence of coronary heart disease. METHODS: This study proposes a system to interpretation clinical examination results for the diagnosis of coronary heart disease based the decision tree algorithm. This system comprises several stages. First, oversampling is carried out by a combination of the synthetic minority oversampling technique (SMOTE), feature selection, and the C4.5 classification algorithm. System testing is done using k-fold cross-validation. The performance parameters are sensitivity, specificity, positive prediction value (PPV), negative prediction value (NPV) and the area under the curve (AUC). RESULTS: The results showed that the performance of the system has a sensitivity of 74.7%, a specificity of 93.7%, a PPV of 74.2%, an NPV of 93.7%, and an AUC of 84.2%. CONCLUSIONS: This study demonstrated that, by using C4.5 algorithms, data can be interpreted in the form of a decision tree, to aid the understanding of the clinician. In addition, the proposed system can provide better performance by category.
Area Under Curve ; Classification ; Coronary Disease* ; Data Mining ; Decision Trees ; Diagnosis* ; Heart Diseases ; Incidence ; Sensitivity and Specificity

OBJECTIVES: Coronary heart disease is the leading cause of death worldwide, and it is important to diagnose the level of the disease. Intelligence systems for diagnosis proved can be used to support diagnosis of the disease. Unfortunately, most of the data available between the level/type of coronary heart disease is unbalanced. As a result system performance is low. METHODS: This paper proposes an intelligence systems for the diagnosis of the level of coronary heart disease taking into account the problem of data imbalance. The first stage of this research was preprocessing, which included resampled non-stratified random sampling (R), the synthetic minority over-sampling technique (SMOTE), clean data out of range attribute (COR), and remove duplicate (RD). The second step was the sharing of data for training and testing using a k-fold cross-validation model and training multiclass classification by the K-star algorithm. The third step was performance evaluation. The proposed system was evaluated using the performance parameters of sensitivity, specificity, positive prediction value (PPV), negative prediction value (NPV), area under the curve (AUC) and F-measure. RESULTS: The results showed that the proposed system provides an average performance with sensitivity of 80.1%, specificity of 95%, PPV of 80.1%, NPV of 95%, AUC of 87.5%, and F-measure of 80.1%. Performance of the system without consideration of data imbalance provide showed sensitivity of 53.1%, specificity of 88,3%, PPV of 53.1%, NPV of 88.3%, AUC of 70.7%, and F-measure of 53.1%. CONCLUSIONS: Based on these results it can be concluded that the proposed system is able to deliver good performance in the category of classification.
Area Under Curve ; Cause of Death ; Classification ; Coronary Disease* ; Diagnosis* ; Heart Diseases ; Intelligence* ; Machine Learning ; Sensitivity and Specificity

Objective: To detect genetic variations among pathogenic Leptospira isolated from rats using 16S rRNA gen as chronometer. Methods: This is an observational study with cross sectional design. Rats samples were taken in Yogyakarta Special Region of Indonesia. Leptospira in the rats was detected by two methods i.e. real time PCR (qPCR) by using primers correspond to16S rRNA gene of Leptospira, and standard PCR by using different set of primer correspond to the 16S rRNA gene of Leptospira. The standard PCR amplicon then subjected for DNA sequencing. Analysis genetic variation was performed using MEGA 6.2. Software. Results: There were 99 DNA samples from rats included in this study. Detection of Leptospira by using qPCR revealed 25 samples positive for pathogenic Leptospira, while only 6 samples were able to be detected using standard PCR. The new primer set correspond to 16S rRNA gene was able to detect specifically pathogenic Leptospira in the rats. Sequencing analysis of 6 PCR amplicons showed that the Leptospira which infect the rats catched in Yogyakarta genetically close related with pathogenic Leptospira which were isolated from human, animal, rodents, and environment. Conclusions: It can be considered that rats are the most important vector and reservoir of Leptospira.

BACKGROUND: International Health Regulations controls international travel including human movement, disease vector, and imported items to prevent the spread of dengue, especially in seaports, airports, and border crossing posts. This study aimed to determine dengue Transovarial Transmission Index (TTI) and distribution of dengue virus in the areas around Adisucipto Airport of Yogyakarta, Indonesia. METHODS: The study was a descriptive analytic study with cross sectional design, conducted by mapping the spread of the dengue virus and identifying TTI in Adisucipto Airport. A total of 145 ovitraps were installed in both perimeter and buffer areas of the airport. Positive Ovitrap Index (OI), TTI, and serotype of dengue virus were examined. The TTI was identified using immunocytochemistry immunoperoxidase streptavidin biotin complex (IISBC) method in mosquito head squash preparations. RESULTS: OI in the buffer area was 32 (45.1%), whereas OI in the perimeter area was 24 (32.4%). The TTI in the buffer and perimeter areas were 21 (18.3%) and 11 (18.9%), respectively. The TTI was found greater in the Aedes aegypti population compared to the Aedes albopictus population, both in the perimeter area (20% versus 16.7%) and the buffer area (20.3% versus 16.1%). Dengue virus serotype-2 (DENV-2) and dengue virus serotype-3 (DENV-3) were predominantly found in Ae. aegypti and Ae. albopictus. CONCLUSIONS Buffer areas of Adisucipto Airport of Yogyakarta have higher risk as breeding sites for Aedes spp., predominantly DENV-2 and DENV-3 serotypes. High OI shows that the areas are likely to have higher risk of developing dengue outbreak.
Aedes ; virology ; Air Travel ; Airports ; Animals ; Cross-Sectional Studies ; Dengue ; transmission ; virology ; Dengue Virus ; classification ; isolation & purification ; Female ; Indonesia ; Mosquito Vectors ; virology ; Ovum ; virology ; Serotyping