scholarly journals Research on Diagnosis Prediction of Traditional Chinese Medicine Diseases Based on Improved Bayesian Combination Model

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhulv Zhang ◽  
Jinghua Li ◽  
Wanting Zheng ◽  
Shaolei Tian ◽  
Yang Wu ◽  
...  
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Medical Information ◽  
Naive Bayes ◽  
Clinical Decision ◽  
Disease Diagnosis ◽  
Naïve Bayes ◽  
Accuracy Rate ◽  
Intelligent Decision Making ◽  
Bayes Algorithm

Traditional Chinese Medicine (TCM) clinical intelligent decision-making assistance has been a research hotspot in recent years. However, the recommendations of TCM disease diagnosis based on the current symptoms are difficult to achieve a good accuracy rate because of the ambiguity of the names of TCM diseases. The medical record data downloaded from ancient and modern medical records cloud platform developed by the Institute of Medical Information on TCM of the Chinese Academy of Chinese Medical Sciences (CACMC) and the practice guidelines data in the TCM clinical decision supporting system were utilized as the corpus. Based on the empirical analysis, a variety of improved Naïve Bayes algorithms are presented. The research findings show that the Naïve Bayes algorithm with main symptom weighted and equal probability has achieved better results, with an accuracy rate of 84.2%, which is 15.2% higher than the 69% of the classic Naïve Bayes algorithm (without prior probability). The performance of the Naïve Bayes classifier is greatly improved, and it has certain clinical practicability. The model is currently available at http://tcmcdsmvc.yiankb.com/.

scholarly journals A Comprehensive Performance Analysis of Various Classifier Models for Coronary Artery Disease Prediction

2021 ◽  
Vol 15 (4) ◽  
pp. 0-0
Keyword(s):  
Heart Disease ◽  
Decision Tree ◽  
Naive Bayes ◽  
Vascular Diseases ◽  
Clinical Decision ◽  
Disease Diagnosis ◽  
Naïve Bayes ◽  
Support Vector ◽  
Disease Prediction ◽  
Weak Classifier

Cardio Vascular Diseases (CVD) is the major reason for the death of the majority of the people in the world. Earlier diagnosis of disease will reduce the mortality rate. Machine learning (ML) algorithms are giving promising results in the disease diagnosis and it is now widely accepted by medical experts as their clinical decision support system. In this work, the most popular ML models are investigated and compared with one other for heart disease prediction based on various metrics. The base classifiers such as Support Vector Machine (SVM), Logistic regression, Naïve Bayes, Decision Tree, K Nearest Neighbour are used for predicting heart disease. In this paper, bagging and boosting techniques are applied over these individual classifiers to improve the performance of the system. With the Cleveland and Statlog datasets, Naive Bayes as the individual classifier gives the maximum accuracy of 85.13%and 84.81% respectively. Bagging technique improves the accuracy of the decision tree which is identified as a weak classifier by 7% and it is a significant improvement in identifying CVD.

scholarly journals Top-k Feature Selction Untuk Deteksi Penyakit Hepatitis Menggunakan Algoritme Naïve Bayes

2020 ◽  
Vol 11 (1) ◽  
pp. 1
Author(s):  
Riska Wibowo ◽  
Henny Indriyawati
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Naive Bayes ◽  
Information Gain ◽  
Naïve Bayes ◽  
Decision Making Process ◽  
Accuracy Rate ◽  
Chemical Substances ◽  
Drugs And Alcohol ◽  
Bayes Algorithm

Abstract. Becoming one of the society health problems in the world, hepatitis is an inflammation liver disease caused by a virus, bacterial infection, chemical substances including drugs and alcohol. In this research, for the dataset of hepatitis having high dimensionality, its value for each attribute was calculated using weight information gain method. Then, the attributes were selected by using top-k methods and were classified by using Naïve Bayes Algorithm respectively. This research showed that 9 out of 20 attributes had chosen to be the highest top-9 with an accuracy rate of 85.57%. Later on, this research can be useful for a consideration in a decision making process for various subjects related to feature selection and Naïve Bayes Algorithm method and also for predicting hepatitis.Keywords: data mining, weight information gain, Naïve Bayes algorithmAbstrak. Penyakit hepatitis merupakan masalah kesehatan masyarakat di dunia. Penyakit hepatitis merupakan penyakit peradangan hati yang disebabkan oleh virus, infeksi bakteri, zat-zat kimia termasuk obat-obatan dan alkohol. Pada penelitian ini, dataset hepatitis yang memiliki data berdimensi tinggi akan dihitung nilai bobot dari masing-masing atribut menggunakan metode weight information gain. Setelah dihitung nilai bobot dilakukan pemilihan atribut, atribut yang dipilih menggunakan metode top-k. Kemudian dilakukan klasifikasi menggunakan algoritme Naïve Bayes. Hasil penelitian menunjukkan dari 20 atribut, terpilih top-9 tertinggi dengan nilai akurasi 85.57%. Dengan adanya penelitian ini dapat digunakan sebagai bahan pertimbangan dan pengambilan keputusan pada berbagai bidang yang berkaitan dengan metode feature selection, algoritme Naïve Bayes, dan di dalam memprediksi penyakit hepatitis.Kata Kunci: data mining, weight information gain, algoritma Naïve Bayes

scholarly journals ICU Patient Prediction for Moving with Decision Tree C4.5 and Naïve Bayes Algorithm

SinkrOn ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 88 ◽  
Author(s):  
Sumpena Sumpena ◽  
Yuma Akbar ◽  
Nirat Nirat ◽  
Mario Hengky
Keyword(s):  
Intensive Care ◽  
Decision Tree ◽  
Medical Information ◽  
Naive Bayes ◽  
Venous Pressure ◽  
Naïve Bayes ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Sepsis Diagnosis ◽  
Bayes Algorithm

Critical patients need intensive care and supervision by the medical team in the Intensive Care Unit (ICU), including ventilators, monitors, Central Venous Pressure (CVP), Electrocardiogram (ECG), Echocardiogram (ECHO), medical supply, and medical information that is fast, precise, and accurate. In the ICU treatment room requires data that needs to be processed and analyzed for decision making. This study analyzed the ventilator, CVP and also Sepsis Diagnosis related to the data of moving patients and patients dying. This study also uses the decision tree algorithm C.45 and Naive Bayes to determine the level of accuracy of patient care and supervision information in the ICU. The results showed that the decision tree algorithm C.45 has an accuracy of 81.55% and Naive Bayes of 81.54%. The decision tree C.45 algorithm has almost the same advantages as the Naive Bayes algorithm.

scholarly journals Performance comparison between naive bayes and k- nearest neighbor algorithm for the classification of Indonesian language articles

2021 ◽  
Vol 10 (2) ◽  
pp. 452
Author(s):  
Titin Winarti ◽  
Henny Indriyawati ◽  
Vensy Vydia ◽  
Febrian Wahyu Christanto
Keyword(s):  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Performance Comparison ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm ◽  
Bayes Algorithm

<span id="docs-internal-guid-210930a7-7fff-b7fb-428b-3176d3549972"><span>The match between the contents of the article and the article theme is the main factor whether or not an article is accepted. Many people are still confused to determine the theme of the article appropriate to the article they have. For that reason, we need a document classification algorithm that can group the articles automatically and accurately. Many classification algorithms can be used. The algorithm used in this study is naive bayes and the k-nearest neighbor algorithm is used as the baseline. The naive bayes algorithm was chosen because it can produce maximum accuracy with little training data. While the k-nearest neighbor algorithm was chosen because the algorithm is robust against data noise. The performance of the two algorithms will be compared, so it can be seen which algorithm is better in classifying documents. The comes about obtained show that the naive bayes algorithm has way better execution with an accuracy rate of 88%, while the k-nearest neighbor algorithm has a fairly low accuracy rate of 60%.</span></span>

scholarly journals Aplikasi Asesmen Calon Debitur menggunakan Naive Bayes di Koperasi Mitra Sejahtera SMK Negeri 1 Kota Sukabumi

2021 ◽  
Vol 10 (1) ◽  
pp. 31-39
Author(s):  
Indra Griha Tofik Isa
Keyword(s):  
System Design ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Test Results ◽  
Accuracy Rate ◽  
Program Testing ◽  
Structured Design ◽  
Bayes Algorithm ◽  
Loan Amount

Cooperatives have an important role in economic development in Indonesia. One of them is the Mitra Sejahtera Cooperative (KMS), which is located in Sukabumi - West Java. The problem that in KMS was the increase in bad credit during the 2015-2019 period which had an impact on decreasing cash circulation flow and income of the KMS. So that in this study focuses on making a prospective debtor assessment application by implementing the Naive Bayes algorithm to provide recommendations on the feasibility of prospective debtors who have the potential for bad credit or not. The training data used are 862 data with parameters of age, gender, loan amount, occupation, income and repayment period. The stages taken include: (1) Research Initiation, (2) Data Selection, (3) Data Pre Processing, (4) System Design, (5) System Implementation, and (6) Program Testing. In system design using structured design, while the implementation of the system uses Microsoft Visual Studio 2012 tools and MySQL database. The test results from the prospective debtor assessment application obtained an accuracy rate of 86%.

scholarly journals A Comprehensive Performance Analysis of Various Classifier Models for Coronary Artery Disease Prediction

2021 ◽  
Vol 15 (4) ◽  
pp. 1-14
Author(s):  
Baranidharan Balakrishnan ◽  
Vinoth Kumar C. N. S.
Keyword(s):  
Heart Disease ◽  
Decision Tree ◽  
Naive Bayes ◽  
Vascular Diseases ◽  
Clinical Decision ◽  
Disease Diagnosis ◽  
Naïve Bayes ◽  
Support Vector ◽  
Disease Prediction ◽  
Weak Classifier

Cardio Vascular Diseases (CVD) is the major reason for the death of the majority of the people in the world. Earlier diagnosis of disease will reduce the mortality rate. Machine learning (ML) algorithms are giving promising results in the disease diagnosis and it is now widely accepted by medical experts as their clinical decision support system. In this work, the most popular ML models are investigated and compared with one other for heart disease prediction based on various metrics. The base classifiers such as Support Vector Machine (SVM), Logistic regression, Naïve Bayes, Decision Tree, K Nearest Neighbour are used for predicting heart disease. In this paper, bagging and boosting techniques are applied over these individual classifiers to improve the performance of the system. With the Cleveland and Statlog datasets, Naive Bayes as the individual classifier gives the maximum accuracy of 85.13%and 84.81% respectively. Bagging technique improves the accuracy of the decision tree which is identified as a weak classifier by 7% and it is a significant improvement in identifying CVD.

scholarly journals IMPLEMENTASI KLASIFIKASI NAIVE BAYES DALAM MEMPREDIKSI LAMA STUDI MAHASISWA (STUDI KASUS : UNIVERSITAS DHYANA PURA)

2021 ◽  
Vol 4 (2) ◽  
pp. 202-209
Author(s):  
Kelvin Hennry Loudry Malelak ◽  
I Made Dwi Ardiada ◽  
Gerson Feoh
Keyword(s):  
Data Mining ◽  
Undergraduate Students ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Accuracy Rate ◽  
Testing Data ◽  
Study Programs ◽  
Bayes Algorithm ◽  
Academic Year

Under normal conditions, undergraduate or undergraduate students from a university can complete their studies for 4 years or 8 semesters. In fact, many students complete their study period of more than 4 years. Is known that in fact in the 2015/2016 academic year there were 744 people who were accepted as students. Of the 744 people who were accepted, 405 people had completed a study period of about 4 years and the remaining 39 people completed their studies for 5 years and 300 of them did not continue their studies. Based on the problem on, so This study implements a classification that can help Dhyana Pura University in predicting the length of study for students who are currently studying in various study programs at Dhyana Pura University. The author's method serves in the classification to predict long student study period is the Naive Bayes algorithm. By using the Java-based Rapid Miner tool to classify graduation data. Then the implementation of data mining which is divided into 968 training data and 193 data testing data with naive Bayes has succeeded in obtaining an accuracy rate of 100% which also has very good parameters.

scholarly journals Penerapan Data Mining untuk Memprediksi Minat Nasabah Terhadap Produk Asuransi Meninggal Dunia dengan Metode Naïve Bayes (Studi Kasus : PT. BNI Life Insurance)

Respati ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. 103
Author(s):  
Ari Hidayatullah, Ena Mudiawati, Muhammad Syafrullah
Keyword(s):  
Data Mining ◽  
Life Insurance ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Insurance Companies ◽  
Insurance Company ◽  
Accuracy Rate ◽  
Customer Data ◽  
Bayes Algorithm ◽  
Business Behavior

INTISASIPendapatan untuk perusahaan asuransi ditentukan oleh jumlah premi yang dibayar oleh nasabah. faktor penting nasabah berupa premi, premi ditentukan dalam persentase atau tarif tertentu. Pada perusahaan asuransi pasti memiliki jumlah data, dan data tersebut sangat penting bagi perusahaan untuk mengetahui kriteria nasabah yang berminat pada asurnsi yang dipasarkan. Dengan adanya informasi dari  data  nasabah  yang  ada,  perusahaan  asuransi  dapat  mengambil  suatu keputusan dalam menerapkan stragi perusahaan diantarnya yaitu menjual produk- produk promo untuk meninggatkan pendapatan perusahaan. Data mining merupakan suatu teknologi yang dapat membantu perusahaan dalam menemukan suatu yang sangat penting dari sekumpulan data. Data mining dapat membentu sautu pola atau membuat suatu sifat perilaku bisnisa yang berguna untuk pengambilan keputusan. Dengan menggunakan metode algoritma Naive Bayes diharapkan bisa membantu perusahaan dalam pengelolaan data nasabah dengan cara mengklasifikasi data nasabah untuk memprediksi minat nasabah dengan tingkat akurasi melebihi 80% dalam memilih produk asuransi meninggal dunia. Kata Kunci: asuransi, baïve bayes, prediksi, data mining.   ABSTRACTIncome for insurance companies is determined by the amount of premium paid by the customer. Important factors for customers in the form of premiums, premiums are determined in certain percentages or rates. The insurance company certainly has the amount of data, and the data is very important for companies to know the criteria of customers who are interested in the insurance marketed. With the information from existing customer data, the insurance company can make a decision in implementing the company's strategy, which is to sell promo products to increase company revenue. Data mining is a technology that can help companies find a very important set of data. Data mining can form a pattern or create a nature of business behavior that is useful for decision making. By using the Naive Bayes algorithm method, it is expected to be able to assist companies in managing customer data by classifying customer data to predict customer interest with an accuracy rate exceeding 80% in choosing a death insurance product. Keywords: insurance, baïve bayes, predictions, data mining..

Emotion Identification between POMS and Multinomial Naive Bayes Algorithm Using Twitter API

2019 ◽  
Vol 7 (7) ◽  
pp. 14-19 ◽  
Author(s):  
Asharani S Dandoti ◽  
Sunil M Sangve
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Emotion Identification ◽  
Bayes Algorithm

Algorithm Comparation of Naive Bayes and Support Vector Machine based on Particle Swarm Optimization in Sentiment Analysis of Freight Forwarding Services

2020 ◽  
Vol 4 (2) ◽  
pp. 362-369
Author(s):  
Sharazita Dyah Anggita ◽  
Ikmah
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
The Public ◽  
Svm Algorithm ◽  
Bayes Algorithm ◽  
Freight Forwarding ◽  
Improved Accuracy

The needs of the community for freight forwarding are now starting to increase with the marketplace. User opinion about freight forwarding services is currently carried out by the public through many things one of them is social media Twitter. By sentiment analysis, the tendency of an opinion will be able to be seen whether it has a positive or negative tendency. The methods that can be applied to sentiment analysis are the Naive Bayes Algorithm and Support Vector Machine (SVM). This research will implement the two algorithms that are optimized using the PSO algorithms in sentiment analysis. Testing will be done by setting parameters on the PSO in each classifier algorithm. The results of the research that have been done can produce an increase in the accreditation of 15.11% on the optimization of the PSO-based Naive Bayes algorithm. Improved accuracy on the PSO-based SVM algorithm worth 1.74% in the sigmoid kernel.

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