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Heart Disease Prediction System Using Decision Tree and naive bayes algorithm

CURRENT MEDICAL IMAGING 2019年第8期15卷 712-717页

作者： Maheswari, Subburaj Pitchai, Ramu Natl Engn Coll Dept Comp Sci & Engn Kovilpatti 628503 Tamil Nadu India BV Raju Inst Technol Dept Comp Sci & Engn Narsapur 502313 Telangana India

The huge information of healthcare data is collected from the healthcare industry which is not "mined" unfortunately to make effective decision making for the identification of hidden information. The end user support system is used as the prediction application for the heart disease and this paper proposes windows through the intelligent prediction system the instance guidance for the heart disease is given to the user. Various symptoms of the heart diseases are fed into the application. The user precedes the processes by checking the specific detail and symptoms of the heart disease. The decision tree (ID3) and navie Hayes techniques in data mining are used to retrieve the details associated with each patient. Based on the accurate result prediction, the performance of the system is analyzed.

关键词： Intelligent prediction system decision tree algorithm knowledge representation data mining naive bayes algorithm heart disease prediction

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A naive bayes algorithm for tissue origin diagnosis (TOD-bayes) of synchronous multifocal tumors in the hepatobiliary and pancreatic system

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INTERNATIONAL JOURNAL OF CANCER 2018年第2期142卷 357-368页

作者： Jiang, Weiqin Shen, Yifei Ding, Yongfeng Ye, Chuyu Zheng, Yi Zhao, Peng Liu, Lulu Tong, Zhou Zhou, Linfu Sun, Shuo Zhang, Xingchen Teng, Lisong Timko, Michael P. Fan, Longjiang Fang, Weijia Zhejiang Univ Sch Med Affiliated Hosp 1 Canc Biotherapy Ctr Hangzhou Zhejiang Peoples R China Zhejiang Univ Inst Bioinformat Hangzhou Zhejiang Peoples R China Zhejiang Univ IBM Biocomputat Lab Hangzhou Zhejiang Peoples R China Zhejiang Univ Res Ctr Air Pollut & Hlth Hangzhou Zhejiang Peoples R China Zhejiang Univ Sch Med Affiliated Hosp 1 Dept Surg Oncol Hangzhou Zhejiang Peoples R China Zhejiang Univ Sch Med Affiliated Hosp 1 Key Lab Precis Diag & Treatment Hepatobiliary & P Hangzhou Zhejiang Peoples R China Zhejiang Univ Sch Med Med Biotechnol Lab Hangzhou Zhejiang Peoples R China Univ Virginia Dept Biol Charlottesville VA USA Univ Virginia Dept Publ Hlth Sci Charlottesville VA USA

Synchronous multifocal tumors are common in the hepatobiliary and pancreatic system but because of similarities in their histological features, oncologists have difficulty in identifying their precise tissue clonal origin through routine histopathological methods. To address this problem and assist in more precise diagnosis, we developed a computational approach for tissue origin diagnosis based on naive bayes algorithm (TOD-bayes) using ubiquitous RNA-Seq data. Massive tissue-specific RNA-Seq data sets were first obtained from The Cancer Genome Atlas (TCGA) and approximate to 1,000 feature genes were used to train and validate the TOD-bayes algorithm. The accuracy of the model was >95% based on tenfold cross validation by the data from TCGA. A total of 18 clinical cancer samples (including six negative controls) with definitive tissue origin were subsequently used for external validation and 17 of the 18 samples were classified correctly in our study (94.4%). Furthermore, we included as cases studies seven tumor samples, taken from two individuals who suffered from synchronous multifocal tumors across tissues, where the efforts to make a definitive primary cancer diagnosis by traditional diagnostic methods had failed. Using our TOD-bayes analysis, the two clinical test cases were successfully diagnosed as pancreatic cancer (PC) and cholangiocarcinoma (CC), respectively, in agreement with their clinical outcomes. Based on our findings, we believe that the TOD-bayes algorithm is a powerful novel methodology to accurately identify the tissue origin of synchronous multifocal tumors of unknown primary cancers using RNA-Seq data and an important step toward more precision-based medicine in cancer diagnosis and treatment.

关键词： synchronous multifocal tumors tissue origin RNA-Seq naive bayes algorithm hepatobiliary and pancreatic system

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Identification of hidden disaster causing factors in coal mine based on naive bayes algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第2期41卷 2823-2831页

作者： Zhao, Yifan Tian, Shuicheng Xian Univ Sci & Technol Coll Safety Sci & Engn Xian Peoples R China Xian Univ Sci & Technol Inst Safety & Emergency Management Xian Peoples R China

In order to overcome the problems of low recognition rate and long recognition time existing in traditional methods, a method for identifying hidden disaster factors in coal mines based on naive bayes algorithm was proposed. The posterior probability of bayesian network is calculated to obtain the maximum value of the posterior probability, so as to judge the categories of hidden disaster factors in coal mines. The method of combining soft and hard threshold functions is used to denoise naive bayes network. Combined with the structural equation of coal mine concealed disaster-causing factors, the index weight of coal mine disaster-causing factors is calculated, and a fast identification model of disaster-causing factors is built to complete the identification. Experimental results show that the quality factors of the proposed method are all higher than 8, the recognition rate is as high as 98%, and the recognition time is basically controlled within 0.8 s.

关键词： naive bayes algorithm coal mine hidden disaster factors identification

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Dynamic human contact prediction based on naive bayes algorithm in mobile social networks

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SOFTWARE-PRACTICE & EXPERIENCE 2020年第11期50卷 2031-2045页

作者： Zeng, Feng Yao, Lan Wu, Baoling Li, Wenjia Meng, Lin Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Hunan Univ Coll Math & Econometr Changsha 410082 Peoples R China New York Inst Technol Dept Comp Sci New York NY USA Ritsumeikan Univ Dept Elect & Comp Engn Kusatsu Japan

Human contact prediction is a challenging task in mobile social networks. The existing prediction methods are based on the static network structure, and directly applying these static prediction methods to dynamic network prediction is bound to reduce the prediction accuracy. In this paper, we extract some important features to predict human contacts and propose a novel human contact prediction method based on naive bayes algorithm, which is suitable for dynamic networks. The proposed method takes the ever-changing structure of mobile social networks into account. First, the past time is partitioned into many periods with equal intervals, and each period has a feature matrix of all node pairs. Then, with the feature matrixes used for classifiers training based on naive bayes algorithm, we can get a classifier for each time period. At last, the different weights are assigned to the classifiers according to their importance to contact prediction, and all classifiers are weighted combination into the final prediction classifier. The extensive experiments are conducted to verify the effectiveness and superiority of the proposed method, and the results show that the proposed method can improve the prediction accuracy and TP Rate to a large extent. Besides, we find that the size of time interval has a certain impact on the clustering coefficient of mobile social networks, which further affects the prediction accuracy.

关键词： clustering coefficient features extraction human contact prediction mobile social networks naive bayes algorithm time interval

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Predicton of MRR & Surface Roughness in Wire EDM Machining using Decision Tree and naive bayes algorithm

Predicton of MRR & Surface Roughness in Wire EDM Machining u...

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3rd IEEE International Conference on Emerging Smart Computing and Informatics (IEEE-ESCI)

作者： Yogesh, L. Arunadevi, M. Prakash, C. P. S. Dayananda Sagar Coll Engn Dept Mech Engn Bangalore Karnataka India

ISBN: (纸本)9781728185194

Manufacturing industries facing problem in optimal selection of process parameters in machining process. Finding optimum process parameters for achieving maximum Material Removal Rate and minimum Surface Roughness is a challenging task and it requires lot of time and energy for experimentation trails or experience. It wastes lot of resources and money, sometimes ends up with negative results. To overcome the above issue, this paper presents an algorithm for prediction of Surface Roughness and Material Removal rate using Decision Tree algorithm and naive bayes algorithm without experimentation. Lot of resources and time can be saved using these machine learning algorithms. In this paper, Material removal rate and Surface roughness of EDM machining of Aluminum composites is predicted using Decision tree algorithm and naive bayes algorithm. Then the model can be used to predict the Material Removal Rate and Surface finish of any combination process parameters before machining process.

关键词： Decision Tree algorithm EDM Optimization naive bayes algorithm Surface Roughness Machine Learning

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Research on E-commerce Customer Churn Based on RFM Model and naive bayes algorithm 8th

Research on E-commerce Customer Churn Based on RFM Model and...

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8th International Conference on Artificial Intelligence and Security (ICAIS)

作者： Tang, Yuan Li, Yupeng Sun, Guang Hunan Univ Finance & Econ Changsha 410000 Peoples R China

ISBN: (纸本)9783031067945;9783031067938

In recent years, with the rapid development of e-commerce, more and more people are engaged in the e-commerce industry. In order to stand out from numerous e-commerce enterprises and retain customers, it is necessary to achieve accurate marketing, so as to segment the market, locate customer groups, and attract and retain customers through formulating marketing strategies. In this paper, RFM model and naive bayes algorithm are used to analyze customer churn. The three indicators of RFM model are relatively independent and have good representativeness for customer classification, and have been widely used in customer classification in various fields. naive bayes algorithm can calculate the probability of loss more easily, so the combination of the two can be used to identify which kind of customers are more likely to lose. Thus help enterprises to implement different marketing strategies for different customer groups, in order to save the cost of enterprises, improve the efficiency of enterprises.

关键词： RFM model naive bayes algorithm Customer churn forecast

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A Chinese text classification system based on naive bayes algorithm

A Chinese text classification system based on Naive Bayes al...

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International Conference on Electronic, Information and Computer Engineering (ICEICE)

作者： Cui, Wei Luzhou Vocat & Tech Coll Dept Informat Technol Luzhou 646005 Sichuan Peoples R China

In this paper, aiming at the characteristics of Chinese text classification, using the ICTCLAS(Chinese lexical analysis system of Chinese academy of sciences) for document segmentation, and for data cleaning and filtering the Stop words, using the information gain and document frequency feature selection algorithm to document feature selection. Based on this, based on the naive bayesian algorithm implemented text classifier, and use Chinese corpus of Fudan University has carried on the experiment and analysis on the system.

关键词： Chinese word segmentation Text categorization Information gain naive bayes algorithm

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PredICT: A Mobile Application for Predicting the Students' Career using naive bayes algorithm 2019

PredICT: A Mobile Application for Predicting the Students' C...

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2nd International Conference on Software Engineering and Information Management (ICSIM) / 2nd International Conference on Big Data and Smart Computing (ICBDSC)

作者： Acerado, Risty M. Marco, Roselia C. Santos, John Richard Carpio, Janina Jasmin Isanan, Hannah Aubrey Technol Inst Philippines Quezon City Metro Manila Philippines

ISBN: (纸本)9781450366427

In today's competitive economy, a lot of graduating students from different programs find it hard to look for the appropriate job after graduation that is related to their undergraduate programs. The proposed PredICT mobile application using the naive bayes algorithm is designed to help the students achieve their career goals by predicting the jobs depending on their skills. With the predicted job, the students can have more understanding of the required skills of the career they would most likely pursue. The result of the tests and evaluation shows that most users strongly agreed on the application's software quality.

关键词： Career Prediction naive bayes algorithm Mobile Application

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Prediction of Cardiac Disease using naive bayes algorithm 2

Prediction of Cardiac Disease using Naive Bayes Algorithm

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2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS)

作者： Ramesh, S. M. Sengottaiyan, N. Vanathi, D. Manoja, R. Tamizharasu, K. Kalyanasundara, P. KPR Inst Engn & Technol Dept ECE Coimbatore Tamil Nadu India Alliance Univ Alliance Coll Engn & Design Fac Sci & Technol Comp Sci Anekal 562106 Karnataka India Nandha Engn Coll Dept CSE Perundurai 638052 Erode India KSR Coll Engn Dept CSE CS Tiruchengode 637215 Tamil Nadu India KSR Coll Engn Dept Comp Sci Tiruchengode 637215 Tamil Nadu India KSR Coll Engn Dept IT Tiruchengode 637215 Tamil Nadu India

ISBN: (纸本)9798350391558;9798350379990

The goal of this work is to use machine learning methods, such as the naive bayes algorithm, to accurately forecast cardiac disease. The proposed work implements K-Nearest Neighbour (KNN), Support Vector Machine (SVM) and naive bayes to perform regression and classification analysis on the data to attain improved accuracy. This revised dataset is used to train the models, and standard metrics are used to thoroughly assess the model's predicted performance. With an accuracy of 88%, the naive bayes algorithm performed better than the KNN algorithm. In the context of forecasting cardiac disorders, this conversation offers a thorough review of the naive bayes method. The implementation generates accurate predictions based on crucial health factors through data preprocessing, feature selection and model training.

关键词： Machine Learning Accuracy Datasets Prediction naive bayes algorithm Cardiac disease

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Using naive bayes algorithm to Students' bachelor Academic Performances Analysis 4

Using Naive Bayes Algorithm to Students' bachelor Academic P...

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4th IEEE International Conference on Engineering Technologies and Applied Sciences (ICETAS)

作者： Razaque, Fahad Soomro, Nareena Shaikh, Shoaib Ahmed Soomro, Safeeullah Samo, Javed Ahmed Kumar, Natesh Dharejo, Huma Indus Univ Dept Comp Karachi Sindh Pakistan Hamdard Univ Karachi Sindh Pakistan Aligarh Inst Technol Karachi Sindh Pakistan AMA Int Univ Coll Comp Studies Salmabad Bahrain

ISBN: (纸本)9781538621073;9781538621066

Academic Data Mining was one of emerging field which comprise procedure of examined students' details by different elements such as earlier semester marks, attendance, assignment, discussion, lab work were of used to improved bachelor academic performance of students, and overcome difficulties of low ranks of bachelor students. It was extracted useful knowledge from bachelor academic students data collected from department of Computing. Subsequently preprocessing data, which was applied data mining techniques to discover classification and clustering. In this study, classification method was described which was based on naive byes algorithm and used for Academic data mining. It was supportive to students along with to lecturers for evaluation of academic performance. It was cautionary method for student's to progress their performance of study.

关键词： Clustering Classification naive bayes algorithm Students Performance

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