Penerapan K-Nearest Neighbor dalam Pendeteksian Abcessus

Abcessus is a collection of neutrophils that do not function anymore and have accumulated in tissue cavities infected with bacteria or parasites. This disease will cause swelling in the part of the skin tissue that contains pus and blood, this is due to the spread of skin tissue with staphylococcus aureus bacteria. The spread of infections carried out by bacteria will result in the release of toxins that cause inflammation and increase blood flow in the infected place. The high circulation of the disease among the community requires a concept of knowledge and information which can later be disseminated to the community so that it can reduce the risk of spreading this disease and can be done as soon as possible early treatment of people suffering from Abcessus. The concept of knowledge that will be formed is by transferring all forms of information and knowledge about Abcessus into the diagnosing application by applying the Expert System science that uses K-Nearest Neighbor analysis, later the method can produce the probability value or probability of diagnosing Abcessus for the symptoms that are felt clinically, of course, the knowledge and probability value of Abcessus will first be determined by an expert or expert in identifying the Abcessus. With the construction of diagnostic applications this can be used as a source to be used by the wider community in dealing with problems regarding diagnosis and knowledge of Abcessus, besides that it can also be used in the analysis of diagnostic conclusions by health or medical officers

Download Full-text

The Study of Hybrid Gaussian CBR Technique and Using in Camshaft Design System

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.108-111.603 ◽

2010 ◽

Vol 108-111 ◽

pp. 603-607

Author(s):

Wei Yan ◽

Xue Qing Li ◽

Xu Guang Tan ◽

De Hui Tong ◽

Qi Gao

Keyword(s):

Expert System ◽

Decision Model ◽

Nearest Neighbor ◽

Design System ◽

Case Based Reasoning ◽

K Nearest Neighbor ◽

Engine Design ◽

Cam Profile ◽

Aided Design ◽

Case Based

In this paper, we propose a hybrid decision model using case-based reasoning augmented the Gaussian and k nearest neighbor methods for aided design camshaft in engine. The hybrid Gaussian k-NN (HGKNN) CBR scheme is designed to compute memberships between cam profile and engine parameters, which provides a more flexible and practical mechanism for reusing the decision knowledge. These methods were implemented in the database application and expert system following the examples of Cam Profile. To get the designed case, the retrieved results were compared and analyzed by HGKNN and k-NN algorithm in the CBR database. It proves the validity of HGKNN and CBR design system is used successfully in engine design process.

Download Full-text

Expert system for diagnosing diphtheria with k-nearest neighbor method

International Journal Artificial Intelligent and Informatics ◽

10.33292/ijarlit.v1i1.4 ◽

2018 ◽

Vol 1 (2) ◽

pp. 45

Author(s):

Chavid Syukri Fatoni ◽

Ema Utami ◽

Ferry Wahyu Wibowo

Keyword(s):

Infectious Disease ◽

Acute Kidney Injury ◽

Expert System ◽

Nearest Neighbor ◽

Kidney Injury ◽

Initial Diagnosis ◽

K Nearest Neighbor ◽

The Common ◽

Special Concern ◽

The Government

The Diphtheria cases have special concern by the Indonesian government and are recorded as an extraordinary case (KLB) in 2017. Diphtheria is an infectious disease and cause complications of dangerous and deadly diseases if have not any treated immediately. Along this time, the communities often underestimate the common symptoms of diseases, such as throat pain, flu, and fever. The similarity of Diphtheria symptoms with common diseases and complications such as myocarditis, obstruction on breath, Acute Kidney Injury (AKI), making Diphtheria are rather difficult to treat due to the infections spread quickly. Some complications of diphtheria can cause a death if have not treated immediately and there must be any identification early for diphtheria. Then, an expert system is needed to help the community and the government in diagnosing the diphtheria. An expert system is an information system containing knowledge from experts in order provide information to be used for consultation. The knowledge from experts in this particular system is used as a basis by the Expert System to answer the questions (consultation). The study used the K-Nearest Neighbor (KNN) method, which the method calculates the similarity value of Diphtheria disease symptom. As the result, it can provide an initial diagnosis for Diphtheria before complications occur. The output of this study is the diagnosis of diphtheria based on the symptoms with the accuracy results of 93.056%, as well as providing an initial diagnosis in order to have immediately treating the diphtheria.

Download Full-text

Comparative Study of Decision Tree, K-Nearest Neighbor, and Modified K-Nearest Neighbor on Jatropha Curcas Plant Disease Identification

Kinetik Game Technology Information System Computer Network Computing Electronics and Control ◽

10.22219/kinetik.v5i1.1012 ◽

2020 ◽

pp. 55-60

Author(s):

Triando Hamonangan Saragih ◽

Diny Melsye Nurul Fajri ◽

Alfita Rakhmandasari

Keyword(s):

Expert System ◽

Decision Tree ◽

Comparative Study ◽

Jatropha Curcas ◽

Plant Disease ◽

Nearest Neighbor ◽

Classification Methods ◽

K Nearest Neighbor ◽

Disease Identification ◽

Accuracy Result

Jatropha Curcas is a very useful plant that can be used as a bio fuel for diesel engines replacing the coal. In Indonesia, there are few plantation that plant Jatropha Curcas. But there is so limited farmers that understand in detail about the disease of Jatropha Curcas and it may cause a big loss during harvesting when the disease occured with no further action. An expert system can help the farmers to identify the lant diseases of Jatropha Curcas. The objective of this research is to compare several identification and classification methods, such as Decision Tree, K-Nearest Neighbor and its modification. The comparison is based on the accuracy. Modified K-Nearest Neighbor method given the best accuracy result that is 67.74%.

Download Full-text

Implementation Of K-Nearest Neighbor - Certainty Factor For Expert System Detection Of Idiopathic Thrombocytopenic Purpura

E3S Web of Conferences ◽

10.1051/e3sconf/202132804009 ◽

2021 ◽

Vol 328 ◽

pp. 04009

Author(s):

Eva Y. Puspaningrum ◽

Budi Nugroho ◽

Dwi Putri Safira

Keyword(s):

Expert System ◽

Idiopathic Thrombocytopenic Purpura ◽

Nearest Neighbor ◽

Autoimmune Disorder ◽

The Body ◽

K Nearest Neighbor ◽

Certainty Factor ◽

Thrombocytopenic Purpura ◽

Parameter Values ◽

Test Scenarios

Idiopathic Thrombocytopenic Purpura (ITP) is an autoimmune disorder. ITP can occur in children and adults. This disease can be fatal because the platelet count is low due to the destruction of excessive platelets so that it can interfere with vital organs and bleeding occurs. The lack of knowledge of ordinary people about ITP disease, so many people assume that bruises and nosebleeds on the body are caused by fatigue. For that, we need a system that can imitate the expertise of an expert in diagnosing this disease based on the symptoms felt. The method used to support the expert system is the K-Nearest Neighbor and Certainty Factor methods which are a combination of 2 methods, where the classification results from the K-Nearest Neighbor method will be given a certainty value by the Certainty Factor method so as to produce a prediction. The results of combining the two methods can produce certainty in the diagnosis. Based on the test results using 3 test scenarios using parameter values k=3, k=5, k=7 and the results obtained the highest accuracy value with parameter value k=7 obtained an accuracy rate of 90,9%.

Download Full-text

Molecular identification of carcinoma of unknown primary (CUP) with gene expression profiling

Journal of Clinical Oncology ◽

10.1200/jco.2007.25.18_suppl.21024 ◽

2007 ◽

Vol 25 (18_suppl) ◽

pp. 21024-21024 ◽

Cited By ~ 1

Author(s):

K. Z. Qu ◽

H. Li ◽

J. D. Whetstone ◽

A. D. Sferruzza ◽

R. A. Bender

Keyword(s):

Gene Expression ◽

Gene Expression Profiling ◽

Expression Profiling ◽

Nearest Neighbor ◽

Gene Profiling ◽

High Likelihood ◽

K Nearest Neighbor ◽

Nearest Neighbor Analysis ◽

Tumor Origin ◽

Profiling Analysis

21024 Background: We previously reported a method for determining the site of tumor origin for CUP by comparing a 92- gene expression profile to that in a database created from 600 primary and metastatic tumor bank specimens of known origin. K-nearest neighbor analysis was used to determine the likelihood of an unknown patient specimen originating from a particular site with the likelihood assigned as a confidence level and reported as high, medium, low, or unclassified. Herein, we report the analysis of the gene expression profiling results from our initial series of clinical CUP specimens. Methods: We reviewed the results of 76 consecutive de-identified patient samples submitted to our laboratory for routine CUP testing. RNA was extracted from the formalin-fixed, paraffin-embedded (FFPE) tissue blocks and cDNA products used in a semi-quantitative real-time PCR to detect 87 tumor-associated genes and 5 reference genes. Gene expression data were then compared with our database and k-nearest neighbor analysis used to identify the 5 closest neighbors. If all 5 or 4/5 were the same, the result was classified as “high likelihood”, 3/5 = “moderate likelihood”, 2/5 = “low likelihood” and none matching was “unclassifiable”. Results: For the 76 clinical CUP samples tested, gene profiling analysis yielded high-likelihood predictions for 34 (45%), moderate for 12 (16%), low for 12 (16%), and unclassified for 14 (18%); amplification was inadequate for 4 (5%) samples. Overall, gene profiling analysis yielded classifiable predictions in 58 (76%) of clinical CUP samples. An occult carcinoid, metastatic melanoma and adenocarcinoma of the endocervix were identified and then found clinically using this assay. Conclusions: Our previous findings indicate that gene expression profiling can correctly identify the site of tumor origin in a high percentage of tumor bank samples. Data from the present study suggests that this approach can identify a primary site of tumor origin in 76% of actual clinical specimens from pathologist-submitted CUP cases. No significant financial relationships to disclose.

Download Full-text

EEG-Based Prediction of the Recovery of Carotid Blood Flow during Cardiopulmonary Resuscitation in a Swine Model

Sensors ◽

10.3390/s21113650 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3650

Author(s):

Heejin Kim ◽

Ki Hong Kim ◽

Ki Jeong Hong ◽

Yunseo Ku ◽

Sang Do Shin ◽

...

Keyword(s):

Blood Flow ◽

Cardiopulmonary Resuscitation ◽

Nearest Neighbor ◽

Polynomial Kernel ◽

Support Vector ◽

Swine Model ◽

K Nearest Neighbor ◽

Carotid Blood Flow ◽

Time Frequency ◽

Svm Model

The recovery of cerebral circulation during cardiopulmonary resuscitation (CPR) is important to improve the neurologic outcomes of cardiac arrest patients. To evaluate the feasibility of an electroencephalogram (EEG)-based prediction model as a CPR feedback indicator of high- or low-CBF carotid blood flow (CBF), the frontal EEG and hemodynamic data including CBF were measured during animal experiments with a ventricular fibrillation (VF) swine model. The most significant 10 EEG parameters in the time, frequency and entropy domains were determined by neighborhood component analysis and Student’s t-test for discriminating high- or low-CBF recovery with a division criterion of 30%. As a binary CBF classifier, the performances of logistic regression, support vector machine (SVM), k-nearest neighbor, random forest and multilayer perceptron algorithms were compared with eight-fold cross-validation. The three-order polynomial kernel-based SVM model showed the best accuracy of 0.853. The sensitivity, specificity, F1 score and area under the curve of the SVM model were 0.807, 0.906, 0.853 and 0.909, respectively. An automated CBF classifier derived from non-invasive EEG is feasible as a potential indicator of the CBF recovery during CPR in a VF swine model.

Download Full-text