scholarly journals Expert system for diagnosing diphtheria with k-nearest neighbor method

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. 

scholarly journals Perbandingan Metode CBR dan Dempster-Shafer pada Sistem Pakar Terintegrasi Layanan Kesehatan

2021 ◽  
Vol 5 (6) ◽  
pp. 1143-1152
Author(s):  
Istiadi Istiadi ◽  
Emma Budi Sulistiarini ◽  
Rudy Joegijantoro ◽  
Affi Nizar Suksmawati
Keyword(s):  
Infectious Disease ◽  
Health Care ◽  
Expert System ◽  
Knowledge Acquisition ◽  
Health Care System ◽  
Nearest Neighbor ◽  
Similarity Measurement ◽  
K Nearest Neighbor ◽  
Minkowski Distance ◽  
Care System

Infectious disease is a very dangerous disease with a high mortality rate. Delays in handling the spread of an infectious disease can be minimized using an expert system. This study uses an expert system as a disease consulting service that is integrated with the health care system. Integration with the health care system is used for the knowledge acquisition process. The knowledge base on the expert system uses patient medical record data obtained through the health care system. The expert system can diagnose infectious diseases of sore throat (Pharyngitis), diphtheria, dengue fever, Typhoid fever, tuberculosis, and leprosy. The knowledge acquisition process produces 43 symptoms. These symptoms are used to diagnose new cases using Case-Based Reasoning (CBR) and Dempster-Shafer methods. In the CBR method, the similarity measurement process is determined by comparing the K-Nearest Neighbor, Minkowski Distance, and 3W-Jaccard similarity measurement methods. The expert system obtains accuracy values ​​for the CBR K-Nearest Neighbor, CBR Minkowski Distance, and CBR 3W-Jaccard methods at a threshold of 70%, respectively 65.71%, 80%, and 85.71%. The average length of retrieve time required for each similarity method is 0.083s, 0.107s, and 6.325s, respectively. While the diagnosis of disease with Dempster-Shafer gets an accuracy value of 88.57%.  

scholarly journals Framing Twitter Public Sentiment on Nigerian Government COVID-19 Palliatives Distribution Using Machine Learning

2021 ◽  
Vol 13 (6) ◽  
pp. 3497
Author(s):  
Hassan Adamu ◽  
Syaheerah Lebai Lutfi ◽  
Nurul Hashimah Ahamed Hassain Malim ◽  
Rohail Hassan ◽  
Assunta Di Vaio ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Primary Objective ◽  
Support Vector ◽  
Standard English ◽  
Emotion Classification ◽  
K Nearest Neighbor ◽  
The Public ◽  
The Government

Sustainable development plays a vital role in information and communication technology. In times of pandemics such as COVID-19, vulnerable people need help to survive. This help includes the distribution of relief packages and materials by the government with the primary objective of lessening the economic and psychological effects on the citizens affected by disasters such as the COVID-19 pandemic. However, there has not been an efficient way to monitor public funds’ accountability and transparency, especially in developing countries such as Nigeria. The understanding of public emotions by the government on distributed palliatives is important as it would indicate the reach and impact of the distribution exercise. Although several studies on English emotion classification have been conducted, these studies are not portable to a wider inclusive Nigerian case. This is because Informal Nigerian English (Pidgin), which Nigerians widely speak, has quite a different vocabulary from Standard English, thus limiting the applicability of the emotion classification of Standard English machine learning models. An Informal Nigerian English (Pidgin English) emotions dataset is constructed, pre-processed, and annotated. The dataset is then used to classify five emotion classes (anger, sadness, joy, fear, and disgust) on the COVID-19 palliatives and relief aid distribution in Nigeria using standard machine learning (ML) algorithms. Six ML algorithms are used in this study, and a comparative analysis of their performance is conducted. The algorithms are Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM), Random Forest (RF), Logistics Regression (LR), K-Nearest Neighbor (KNN), and Decision Tree (DT). The conducted experiments reveal that Support Vector Machine outperforms the remaining classifiers with the highest accuracy of 88%. The “disgust” emotion class surpassed other emotion classes, i.e., sadness, joy, fear, and anger, with the highest number of counts from the classification conducted on the constructed dataset. Additionally, the conducted correlation analysis shows a significant relationship between the emotion classes of “Joy” and “Fear”, which implies that the public is excited about the palliatives’ distribution but afraid of inequality and transparency in the distribution process due to reasons such as corruption. Conclusively, the results from this experiment clearly show that the public emotions on COVID-19 support and relief aid packages’ distribution in Nigeria were not satisfactory, considering that the negative emotions from the public outnumbered the public happiness.

scholarly journals Twitter sentiment analysis of the relocation of Indonesia's capital city

2020 ◽  
Vol 9 (4) ◽  
pp. 1620-1630
Author(s):  
Edi Sutoyo ◽  
Ahmad Almaarif
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Nearest Neighbor ◽  
Point Of View ◽  
Capital City ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Evaluation Algorithm ◽  
The Government ◽  
The Many

Indonesia has a capital city which is one of the many big cities in the world called Jakarta. Jakarta's role in the dynamics that occur in Indonesia is very central because it functions as a political and government center, and is a business and economic center that drives the economy. Recently the discourse of the government to relocate the capital city has invited various reactions from the community. Therefore, in this study, sentiment analysis of the relocation of the capital city was carried out. The analysis was performed by doing a classification to describe the public sentiment sourced from twitter data, the data is classified into 2 classes, namely positive and negative sentiments. The algorithms used in this study include Naïve Bayes classifier, logistic regression, support vector machine, and K-nearest neighbor. The results of the performance evaluation algorithm showed that support vector machine outperformed as compared to 3 algorithms with the results of Accuracy, Precision, Recall, and F-measure are 97.72%, 96.01%, 99.18%, and 97.57%, respectively. Sentiment analysis of the discourse of relocation of the capital city is expected to provide an overview to the government of public opinion from the point of view of data coming from social media. 

scholarly journals Identification and Classification of Pathogenic Bacteria Using the K-Nearest Neighbor Method

2021 ◽  
Vol 5 (1) ◽  
pp. 60-70
Author(s):  
Diana Rahmawati ◽  
Mutiara Puspa Putri I ◽  
Miftachul Ulum ◽  
Koko Joni
Keyword(s):  
Pathogenic Bacteria ◽  
Nearest Neighbor ◽  
Ocular Lens ◽  
K Nearest Neighbor ◽  
Image Capture ◽  
Chebyshev Method ◽  
Living Things ◽  
Auto Focus ◽  
The Government

Bacteria are a group of living things or organisms that do not have a core covering. In the grouping, some bacteria are pathogenic. With a microscopic size, many pathogenic bacteria are found around and spread through the food eaten or by touching objects around them, then cause diseases such as diarrhea, vomiting, and others. As a more effective effort to help the government and society prevent disease caused by pathogenic bacteria, a system for the identification and classification of pathogenic bacteria K-Nearest Neighbor was created. This system uses a biological microscope that is attached to a webcam camera above the ocular lens as a tool to see bacterial objects and assist in bacterial capture. Rough player rotates automatically (auto-focus) in image capture. In the process of classification and identifying bacteria, the K-Nearest Neighbor method is used, which is a method with the calculation of the nearest neighbor or calculation based on the level of similarity to the dataset. In this study, the bacteria vibrio chlorae, staphylococcus aereus, and streptococcus m. with the highest accuracy is the K = 9 value of 97.77% using the Chebyshev method.

scholarly journals Prediction System for the Spread of Corona Virus in Central Java with K-Nearest Neighbor (KNN) Method

2021 ◽  
Vol 2 (3) ◽  
pp. 80-85
Author(s):  
Farid Fitriyadi ◽  
Muqorobin Muqorobin
Keyword(s):  
Nearest Neighbor ◽  
Current Data ◽  
Problem Analysis ◽  
Prediction System ◽  
K Nearest Neighbor ◽  
Religious Activities ◽  
Literature Study ◽  
Central Java ◽  
Corona Virus ◽  
The Government

Abstract—Corona Virus is currently spreading very rapidly in many parts of Indonesia, including Central Java Province. According to the current data of corona database in Central Java, today on 17th of August 2021, the number of confirmed cases is; Confirmed in Treatment (Active Cases): 16.344, Confirmed Recovered: 408.697, and Confirmed Dead: 29.148. Therefore, the total number of cases is 454.189, obtained from the sum of the number of being treated, recovered, and dead. Corona Virus is a collection of viruses that can infect the respiratory system, generally mild, such as common cold, although, some forms of diseases like; SARS, MERS, and COVID-19 are more deadly. In anticipating this case, the government has created some policies which include; limiting activities outside the house, having school activities done from home, working from home, and even having religious activities done from home too. The purpose of this study was to predict the possible rate of new cases in one of Central Java areas with confirmed cases of corona virus. Thus, it can be used as information material for the public to anticipate early. The research method applied in this research is problem analysis and literature study, data collection and implementation. The application of the K-Nearest Neighbor (KNN) method is expected to be able to predict the level of spread of COVID-19 in Central Java. The results of the research on testing the prediction system for the new cases level were tested in the Sragen area. Testing is carried out by taking samples for new cases, namely Kudu Regency/City, Confirmed: 17,599, Treated: 89, Recovered: 18,303, Died: 1,721, Suspected: 87 and Discarded Suspected: 1,711. After doing the prediction with K-NN algorithm, it showed the Condition: High.

scholarly journals 7. Infectious Disease and Acute Kidney Injury.

2010 ◽  
Vol 99 (5) ◽  
pp. 977-983
Author(s):  
Atsuhiro Yoshida
Keyword(s):  
Infectious Disease ◽  
Acute Kidney Injury ◽  
Kidney Injury

Sa1026 Comparison Between the Common Criterion and the AKIN Criteria in the Definition of Acute Kidney Injury in Hospitalized Patients With Cirrhosis and Ascites

2012 ◽  
Vol 142 (5) ◽  
pp. S-949-S-950
Author(s):  
Silvia Rosi ◽  
Salvatore Piano ◽  
Filippo Morando ◽  
Anna Chiara Frigo ◽  
Silvano Fasolato ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Kidney Injury ◽  
Hospitalized Patients ◽  
The Common ◽  
Common Criterion ◽  
Definition Of

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

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.

scholarly journals The The Classification of Acute Respiratory Infection (ARI) Bacteria Based on K-Nearest Neighbor

2021 ◽  
Vol 12 (2) ◽  
pp. 91
Author(s):  
Zilvanhisna Emka Fitri ◽  
Lalitya Nindita Sahenda ◽  
Pramuditha Shinta Dewi Puspitasari ◽  
Prawidya Destarianto ◽  
Dyah Laksito Rukmi ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Respiratory Infection ◽  
Acute Respiratory Infection ◽  
Nearest Neighbor ◽  
Bacterial Count ◽  
Research Process ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Number Of Patients ◽  
Knn Classification

Acute Respiratory Infection (ARI) is an infectious disease. One of the performance indicators of infectious disease control and handling programs is disease discovery. However, the problem that often occurs is the limited number of medical analysts, the number of patients, and the experience of medical analysts in identifying bacterial processes so that the examination is relatively longer. Based on these problems, an automatic and accurate classification system of bacteria that causes Acute Respiratory Infection (ARI) was created. The research process is preprocessing images (color conversion and contrast stretching), segmentation, feature extraction, and KNN classification. The parameters used are bacterial count, area, perimeter, and shape factor. The best training data and test data comparison is 90%: 10% of 480 data. The KNN classification method is very good for classifying bacteria. The highest level of accuracy is 91.67%, precision is 92.4%, and recall is 91.7% with three variations of K values, namely K = 3, K = 5, and K = 7.

scholarly journals Management of acute kidney injury in patients with COVID-19 or CKD stage 1-4 patients with acute kidney injury in the COVID-19 pandemic: Adapted clinical guidelines of the Ukrainian Association of Nephrologists and Kidney Transplant Specialists

2020 ◽  
pp. 20-27
Author(s):  
M. Kolesnyk ◽  
I. Dudar ◽  
N. Stepanova ◽  
E. Krasyuk ◽  
Yu. Gonchar ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Acute Kidney Injury ◽  
Mortality Rate ◽  
Medical Care ◽  
Kidney Transplant ◽  
Clinical Guidelines ◽  
Kidney Injury ◽  
Ckd Stage ◽  
Infectious Disease Specialists ◽  
Stage 1

Acute kidney injury (AKI) is diagnosed in 1–40% of the COVID-19 patients; from 2% to 10% of the patients are required renal replacement therapy (RRT). The mortality rate in this category of patients reached 88%. Early AKI detection in the patients with COVID-19, followed by the use of preventive and therapeutic measures to minimize the incidence or progression is a significant key to reduce the mortality rate and transformation of AKI into chronic kidney disease (CKD). The expert group of the Ukrainian Association of Nephrologists and Kidney Transplant Specialists has created the adapted clinical guidelines for the management of acute kidney injury in patients with COVID-19 or CKD stage 1-4 patients with acute kidney injury in the COVID-19 pandemic. These guidelines are intended for family physicians, general practitioners, physicians, cardiologists, nephrologists, intensivists, endocrinologists, infectious disease specialists and other professionals involved in the provision of specialized medical care to the patients with COVID-19. Specialized medical care for AKI patients infected COVID-19 should be provided by a multidisciplinary team, which has to involve nephrologists, infectious disease specialists and intensivists.

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