scholarly journals Comparing Performance of Data Mining Algorithms in Prediction Heart Diseases

2015 ◽  
Vol 5 (6) ◽  
pp. 1569 ◽  
Author(s):  
Moloud Abdar ◽  
Sharareh R. Niakan Kalhori ◽  
Tole Sutikno ◽  
Imam Much Ibnu Subroto ◽  
Goli Arji
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Decision Tree ◽  
Heart Diseases ◽  
Support Vector ◽  
Data Mining Algorithm ◽  
Network Support ◽  
Data Mining Algorithms ◽  
Mining Algorithms ◽  
Analysis Models

Heart diseases are among the nation’s leading couse of mortality and moribidity. Data mining teqniques can predict the likelihood of patients getting a heart disease. The purpose of this study is comparison of different data mining algorithm on prediction of heart diseases. This work applied and compared data mining techniques to predict the risk of heart diseases. After feature analysis, models by five algorithms including decision tree (C5.0), neural network, support vector machine (SVM), logistic regression and k-nearest neighborhood (KNN) were developed and validated. C5.0 Decision tree has been able to build a model with greatest accuracy 93.02%, KNN, SVM, Neural network have been 88.37%, 86.05% and 80.23% respectively. Produced results of decision tree can be simply interpretable and applicable; their rules can be understood easily by different clinical practitioner.

scholarly journals A Review Study on Data Mining Algorithms for Prediction Diseases

2020 ◽  
pp. 504-507
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Support Vector Machine ◽  
Support Vector ◽  
Healthcare Industry ◽  
Network Support ◽  
Data Mining Algorithms ◽  
Review Study ◽  
Using Data ◽  
Mining Algorithms

The healthcare industry assembles massive volume of healthcare information or data that circulate the information into useful data. In everyday life several factors that affect the human diseases. Hospitals are producing large amount of information related to patients. This paper describes the various data mining algorithms such as neural network, support vector machine, KNN, decision tree etc. and provides an overall brief of the existing work. The major advantage of using data mining is that to identify the structures.

Acoustic Signature Based Weld Quality Monitoring for SMAW Process Using Data Mining Algorithms

2015 ◽  
Vol 813-814 ◽  
pp. 1104-1113 ◽  
Author(s):  
A. Sumesh ◽  
Dinu Thomas Thekkuden ◽  
Binoy B. Nair ◽  
K. Rameshkumar ◽  
K. Mohandas
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Welding Process ◽  
Machine Learning Algorithms ◽  
Steel Plates ◽  
Support Vector ◽  
Welding Parameters ◽  
Process Data ◽  
Data Mining Algorithms ◽  
Mining Algorithms

The quality of weld depends upon welding parameters and exposed environment conditions. Improper selection of welding process parameter is one of the important reasons for the occurrence of weld defect. In this work, arc sound signals are captured during the welding of carbon steel plates. Statistical features of the sound signals are extracted during the welding process. Data mining algorithms such as Naive Bayes, Support Vector Machines and Neural Network were used to classify the weld conditions according to the features of the sound signal. Two weld conditions namely good weld and weld with defects namely lack of fusion, and burn through were considered in this study. Classification efficiencies of machine learning algorithms were compared. Neural network is found to be producing better classification efficiency comparing with other algorithms considered in this study.

Review of Data Mining Techniques Used in Healthcare

2021 ◽  
pp. 1-26
Author(s):  
Usha Gupta ◽  
Kamlesh Sharma
Keyword(s):  
Data Mining ◽  
Vital Role ◽  
Mining Machine ◽  
Support Vector ◽  
Data Set ◽  
Data Mining Techniques ◽  
Network Support ◽  
Data Mining Algorithms ◽  
Clinical Databases ◽  
Mining Algorithms

Data mining plays a vital role in converting the medical data like text, image, and graphs into meaningful new data, which helps to take the better decision. In this chapter, an overview of the current research is discussed using the data mining techniques for the finding, analysis, and prediction of various diseases. The focus of this study is to identify the well-performing data mining algorithms used on medical and clinical databases. Multiple algorithms have been identified: text-based mining, association rule-based mining, pattern-based mining, keyword-based mining, machine learning, neural network support vector machine, apriori algorithm, k-means clustering, and natural language. Analyses of the algorithm show that there is no single algorithm or model more suitable for diagnosing or predicting diseases. In some scenarios, some algorithms work very well but not in another data set. There are many examples in clinical or medical research where the combination of different algorithms gives good results.

Heart disease prediction using stacked ensemble technique

2020 ◽  
Vol 39 (6) ◽  
pp. 8249-8257
Author(s):  
R. Aravind Vasudev ◽  
B. Anitha ◽  
G. Manikandan ◽  
B. Karthikeyan ◽  
Logesh Ravi ◽  
...  
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Medical Information ◽  
Heart Diseases ◽  
Data Mining Algorithm ◽  
Efficient Manner ◽  
Data Set ◽  
Ensemble Technique ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Heart diseases are one of the crucial diseases that may cause fatality in both men and women. About 12 million deaths occur across the world due to heart diseases. With the advancement in information technology, it is possible for the Healthcare industry to store enormous volume of data containing millions of patient’s medical information along with their treatment details. If utilized in an efficient manner, this information helps the doctors to diagnose the diseases in a precise manner. Data mining algorithms are employed to analyse huge data sets and to discover unseen patterns. Data mining plays an essential role in medical diagnosis. Doctors bank on different computer models which uses data mining algorithms to prefigure different kinds of diseases in patients. So, the need is to design a methodical data mining algorithm that helps for better forecast of diseases. The main goal of this work is to create an ensemble of algorithms which results in better accuracy. The ensemble is constructed by making use of stacking ensemble technique, which comprises of two categorization algorithms namely Naïve Bayes and Artificial Neural Network. The Cleveland heart disease data set acquired from UCI machine learning repository containing 14 attributes and 303 instances is given as input to these algorithms. From our experimental analysis it is evident that the proposed ensemble scheme results in a better accuracy.

scholarly journals Osteoporosis Risk Prediction Using Data Mining Algorithms

2020 ◽  
Author(s):  
Efat Jabarpour ◽  
Amin Abedini ◽  
Abbasali Keshtkar
Keyword(s):  
Data Mining ◽  
Personal Information ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Data Mining Algorithms ◽  
Industry Standard ◽  
Disease Information ◽  
Increased Risk ◽  
Using Data ◽  
Mining Algorithms

Introduction: Osteoporosis is a disease that reduces bone density and loses the quality of bone microstructure leading to an increased risk of fractures. It is one of the major causes of inability and death in elderly people. The current study aims at determining the factors influencing the incidence of osteoporosis and providing a predictive model for the disease diagnosis to increase the diagnostic speed and reduce diagnostic costs. Methods: An Individual's data including personal information, lifestyle, and disease information were reviewed. A new model has been presented based on the Cross-Industry Standard Process CRISP methodology. Besides, Support Vector Machine (SVM) and Bayes methods (Tree Augmented Naïve Bayes (TAN)) and Clementine12 have been used as data mining tools. Results: Some features have been detected to affect this disease. The rules have been extracted that can be used as a pattern for the prediction of the patients' status. Classification precision was calculated to be 88.39% for SVM, and 91.29% for  (TAN) when the precision of  TAN  is higher comparing to other methods. Conclusion: The most effective factors concerning osteoporosis are detected and can be used for a new sample with defined characteristics to predict the possibility of osteoporosis in a person.  

scholarly journals A Novel Design of Classification of Coronary Artery Disease Using Deep Learning and Data Mining Algorithms

2021 ◽  
Vol 35 (3) ◽  
pp. 209-215
Author(s):  
Pratibha Verma ◽  
Vineet Kumar Awasthi ◽  
Sanat Kumar Sahu
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Deep Learning ◽  
Regression Tree ◽  
Principal Component ◽  
Classification And Regression Tree ◽  
Support Vector ◽  
Data Mining Algorithms ◽  
R Programming ◽  
Hidden Layer

Data mining techniques are included with Ensemble learning and deep learning for the classification. The methods used for classification are, Single C5.0 Tree (C5.0), Classification and Regression Tree (CART), kernel-based Support Vector Machine (SVM) with linear kernel, ensemble (CART, SVM, C5.0), Neural Network-based Fit single-hidden-layer neural network (NN), Neural Networks with Principal Component Analysis (PCA-NN), deep learning-based H2OBinomialModel-Deeplearning (HBM-DNN) and Enhanced H2OBinomialModel-Deeplearning (EHBM-DNN). In this study, experiments were conducted on pre-processed datasets using R programming and 10-fold cross-validation technique. The findings show that the ensemble model (CART, SVM and C5.0) and EHBM-DNN are more accurate for classification, compared with other methods.

Applying data mining algorithms to real estate appraisals: a comparative study

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Thiago Cesar de Oliveira ◽  
Lúcio de Medeiros ◽  
Daniel Henrique Marco Detzel
Keyword(s):  
Data Mining ◽  
Real Estate ◽  
Support Vector ◽  
Predictive Capacity ◽  
Content Type ◽  
Data Mining Algorithms ◽  
Wide Range ◽  
Very Large Databases ◽  
Mining Algorithms ◽  
Statistical Results

Purpose Real estate appraisals are becoming an increasingly important means of backing up financial operations based on the values of these kinds of assets. However, in very large databases, there is a reduction in the predictive capacity when traditional methods, such as multiple linear regression (MLR), are used. This paper aims to determine whether in these cases the application of data mining algorithms can achieve superior statistical results. First, real estate appraisal databases from five towns and cities in the State of Paraná, Brazil, were obtained from Caixa Econômica Federal bank. Design/methodology/approach After initial validations, additional databases were generated with both real, transformed and nominal values, in clean and raw data. Each was assisted by the application of a wide range of data mining algorithms (multilayer perceptron, support vector regression, K-star, M5Rules and random forest), either isolated or combined (regression by discretization – logistic, bagging and stacking), with the use of 10-fold cross-validation in Weka software. Findings The results showed more varied incremental statistical results with the use of algorithms than those obtained by MLR, especially when combined algorithms were used. The largest increments were obtained in databases with a large amount of data and in those where minor initial data cleaning was carried out. The paper also conducts a further analysis, including an algorithmic ranking based on the number of significant results obtained. Originality/value The authors did not find similar studies or research studies conducted in Brazil.

scholarly journals Perbandingan Teknik Klasifikasi Neural Network, Support Vector Machine, dan Naive Bayes dalam Mendeteksi Kanker Payudara

2020 ◽  
Vol 7 (1) ◽  
pp. 53
Author(s):  
Derisma Derisma ◽  
Fajri Febrian
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Data Mining ◽  
Support Vector Machine ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Accuracy Rate ◽  
Cancer Disease ◽  
Network Support

Abstrak: Kanker payudara merupakan jenis kanker yang sering ditemukan oleh kebanyakan wanita. Di Indonesia Kanker payudara menempati urutan pertama pada pasien rawat inap di seluruh rumah sakit. Tujuan dari penelitian ini adalah melakukan diagnosis penyakit kanker payudara berbasis komputasi yang dapat menghasilkan bagaimana kondisi kanker seseorang berdasarkan akurasi algoritma. Penelitian ini menggunakan pemrograman orange python dan dataset Wisconsin Breast Cancer untuk pemodelan klasifikasi kanker payudara. Metode data mining yang diterapkan yaitu Neural Network, Support Vector Machine, dan Naive Bayes. Dalam penelitian ini didapat algoritma klasifikasi terbaik yaitu algoritma Kernel SVM dengan tingkat akurasi sebesar  98.9 % dan algoritma terendah yaitu Naive Bayes senilai 96.1 %.   Kata kunci: kanker payudara, neural network, support vector machine, naive bayes   Abstract: Breast cancer is a type of cancer that mostly found in many women. In Indonesia, breast cancer ranks first in hospitalized patients at every hospital. This study aimed to conduct a computation-based diagnose of breast cancer disease that could produce the state of cancer of an individual based on the accuracy of algorithm. This study used python orange programming and Wisconsin Breast Cancer dataset for a modeling and application of breast cancer classification. The data mining methods that were applied in this study were Neural Network, Support Vector Machine, dan Naive Bayes. In this study, Kernel SVM’s algorithm was the best classification algorithm of breast cancer disease with 98.9 % accuracy rate and Naïve Beyes was the lowest with 96.1 % of accuracy rate.   Keywords: breast cancer, neural network, support vector machine, naive bayes

scholarly journals Smart Hospital for Heart Disease Prediction Using IOT

2019 ◽  
Vol 8 (9) ◽  
pp. 321-326 ◽  
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Heart Diseases ◽  
Data Extraction ◽  
Mobile App ◽  
Data Mining Algorithms ◽  
Healthcare Monitoring ◽  
Time Required ◽  
Network Technologies ◽  
Mining Algorithms

The Internet of Things (IoT) is inter communication of embedded devices using various network technologies. The IoT technology is all set to become the upcoming trend in the future. We are proposing a healthcare monitoring system consisting of ECG Sensors. The parameters which are having a significant amount of importance are sensed by the ECG sensors which are vital for remote monitoring of patient. A mobile app observation is used to continuously monitor the ECG of the patient and various data extraction techniques are performed on the ECG wave to extract attributes to correctly predict heart diseases. .Data mining with its various algorithms reduce the extra efforts and time required to conduct various tests to detect diseases.. Data is collected from ECG sensors. The data is stored onto s storage medium where data mining algorithms are performed on the data collected. These algorithms predict whether the patient has any heart disease. The results can be referred by the doctors for diagnosis purpose. By using IOT technology and data mining algorithms the predication of heart disease is going to do in system

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