scholarly journals Support vector machine the most fruitful algorithm for prognosticating heart disorder

2018 ◽  
Vol 7 (2.26) ◽  
pp. 48
Author(s):  
M Murugesan ◽  
R Elankeerthana
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Nearest Neighbor ◽  
Heart Diseases ◽  
Support Vector ◽  
Human Society ◽  
K Nearest Neighbor ◽  
Healthcare Organizations ◽  
Life Threatening ◽  
Medical Examiners

One of the wealthiest areas of research is Data mining that is more popular in healthcare organizations. Heart disease is the main outcome of death in the human society over the recent years. Heart disease is serious life threatening diseases that result to death. In order to save a pan-tient’s life, the doctors and medical examiners are being taking many efforts. The consultant of doctor’s determination can make without the advice of specialists because of the software develop by the advancement in computer technology. In most of the papers, Data mining tech-niques used in the existing method in the research are Naive Bayes, Decision tree, J48, K-Nearest Neighbor (K-NN) (or) Lazy IBK algo-rithms to predict heart diseases. In this paper, support vector machines (SVM) technique will produce the most accuracy prediction rate for heart diseases while comparing to all the other techniques used in data mining. 

scholarly journals Heart Disease Prediction using Neural Network

2021 ◽  
Vol 9 (VI) ◽  
pp. 1907-1910
Author(s):  
Ganesh Nanekar
Keyword(s):  
Heart Disease ◽  
Nearest Neighbor ◽  
Heart Diseases ◽  
Machine Learning Techniques ◽  
Whole Body ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Monthly Basis ◽  
Related Data ◽  
Learning Techniques

Heart is the next major organ comparing to brain which has more priority in Human body. It pumps the blood and supplies to all organs of the whole body. Prediction of occurrences of heart diseases in medical field is significant work. Data analytics is useful for prediction from more information and it helps medical Centre to predict of various disease. Huge amount of patient related data is maintained on monthly basis. The stored data can be useful for source of predicting the occurrence of future disease. Some of the data mining and machine learning techniques are used to predict the heart disease, such as Decision tree, Fuzzy Logic, K-Nearest Neighbor (KNN), Naïve Bayes and Support Vector Machine (SVM). This paper provides an insight of the existing algorithms and implements hybrid algorithms to improve accuracy significantly.

Data Mining Approach to Analyze COVID-19 Clinical Dataset

2021 ◽  
Vol 15 (6) ◽  
pp. 1812-1819
Author(s):  
Azita Yazdani ◽  
Ramin Ravangard ◽  
Roxana Sharifian
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Clinical Signs ◽  
Study Data ◽  
Mining Machine ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Mining Approach

The new coronavirus has been spreading since the beginning of 2020 and many efforts have been made to develop vaccines to help patients recover. It is now clear that the world needs a rapid solution to curb the spread of COVID-19 worldwide with non-clinical approaches such as data mining, enhanced intelligence, and other artificial intelligence techniques. These approaches can be effective in reducing the burden on the health care system to provide the best possible way to diagnose and predict the COVID-19 epidemic. In this study, data mining models for early detection of Covid-19 in patients were developed using the epidemiological dataset of patients and individuals suspected of having Covid-19 in Iran. C4.5, support vector machine, Naive Bayes, logistic regression, Random Forest, and k-nearest neighbor algorithm were used directly on the dataset using Rapid miner to develop the models. By receiving clinical signs, this model diagnosis the risk of contracting the COVID-19 virus. Examination of the models in this study has shown that the support vector machine with 93.41% accuracy is more efficient in the diagnosis of patients with COVID-19 pandemic, which is the best model among other developed models. Keywords: COVID-19, Data mining, Machine Learning, Artificial Intelligence, Classification

scholarly journals Heart Disease Prediction Using Machine Learning

2021 ◽  
pp. 267-276
Author(s):  
Baban. U. Rindhe ◽  
Nikita Ahire ◽  
Rupali Patil ◽  
Shweta Gagare ◽  
Manisha Darade
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Heart Disease ◽  
Heart Diseases ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Whole Body ◽  
Support Vector ◽  
Learning Techniques

Heart-related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need fora reliable, accurate, and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart-related diseases. Heart is the next major organ comparing to the brain which has more priority in the Human body. It pumps the blood and supplies it to all organs of the whole body. Prediction of occurrences of heart diseases in the medical field is significant work. Data analytics is useful for prediction from more information and it helps the medical center to predict various diseases. A huge amount of patient-related data is maintained on monthly basis. The stored data can be useful for the source of predicting the occurrence of future diseases. Some of the data mining and machine learning techniques are used to predict heart diseases, such as Artificial Neural Network (ANN), Random Forest,and Support Vector Machine (SVM).Prediction and diagnosingof heart disease become a challenging factor faced by doctors and hospitals both in India and abroad. To reduce the large scale of deaths from heart diseases, a quick and efficient detection technique is to be discovered. Data mining techniques and machine learning algorithms play a very important role in this area. The researchers accelerating their research works to develop software with thehelp of machine learning algorithms which can help doctors to decide both prediction and diagnosing of heart disease. The main objective of this research project is to predict the heart disease of a patient using machine learning algorithms.

scholarly journals A hybrid cost-sensitive ensemble for heart disease prediction

2021 ◽  
Author(s):  
Zhenya Qi ◽  
Zuoru Zhang
Keyword(s):  
Heart Disease ◽  
Nearest Neighbor ◽  
Statistical Tests ◽  
Disease Diagnosis ◽  
Medical Decision ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Misclassification Cost ◽  
Promising Alternative ◽  
Relief Algorithm

Abstract Background: Heart disease is the primary cause of morbidity and mortality in the world. It includes numerous problems and symptoms. The diagnosis of heart disease is difficult because there are too many factors to analyze. What's more, the misclassification cost could be very high. Methods: A cost-sensitive ensemble method was proposed to improve the efficiency of diagnosis and reduce the misclassification cost. The proposed method contains five heterogeneous classifiers: random forest, logistic regression, support vector machine, extreme learning machine and k-nearest neighbor. T-test was used to investigate if the performance of the ensemble was better than individual classifiers and the contribution of Relief algorithm. Results: The best performance was achieved by the proposed method according to ten-fold cross validation. The statistical tests demonstrated that the performance of the proposed ensemble was significantly superior to individual classifiers, and the efficiency of classification was distinctively improved by Relief algorithm. Conclusions: The proposed ensemble gained significantly better results compared with individual classifiers and previous studies, which implies that it can be used as a promising alternative tool in medical decision making for heart disease diagnosis.

scholarly journals A hybrid cost-sensitive ensemble for heart disease prediction

2020 ◽  
Author(s):  
Zhenya Qi ◽  
Zuoru Zhang
Keyword(s):  
Heart Disease ◽  
Nearest Neighbor ◽  
Statistical Tests ◽  
Disease Diagnosis ◽  
Medical Decision ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Misclassification Cost ◽  
Promising Alternative ◽  
Relief Algorithm

Abstract Background: Heart disease is the primary cause of morbidity and mortality in the world. It includes numerous problems and symptoms. The diagnosis of heart disease is difficult because there are too many factors to analyze. What's more, the misclassification cost could be very high. Methods: A cost-sensitive ensemble method was proposed to improve the efficiency of diagnosis and reduce the misclassification cost. The proposed method contains five heterogeneous classifiers: random forest, logistic regression, support vector machine, extreme learning machine and k-nearest neighbor. T-test was used to investigate if the performance of the ensemble was better than individual classifiers and the contribution of Relief algorithm. Results: The best performance was achieved by the proposed method according to ten-fold cross validation. The statistical tests demonstrated that the performance of the proposed ensemble was significantly superior to individual classifiers, and the efficiency of classification was distinctively improved by Relief algorithm. Conclusions: The proposed ensemble gained significantly better results compared with individual classifiers and previous studies, which implies that it can be used as a promising alternative tool in medical decision making for heart disease diagnosis.

scholarly journals Data mining, fuzzy AHP and TOPSIS for optimizing taxpayer supervision

2020 ◽  
Vol 18 (1) ◽  
pp. 75
Author(s):  
M. Jupri ◽  
Riyanarto Sarno
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Fuzzy Ahp ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Time Required ◽  
Mining Algorithms

The achievement of accepting optimal tax need effective and efficient tax supervision can be achieved by classifying taxpayer compliance to tax regulations. Considering this issue, this paper proposes the classification of taxpayer compliance using data mining algorithms; i.e. C4.5, Support Vector Machine, K-Nearest Neighbor, Naive Bayes, and Multilayer Perceptron based on the compliance of taxpayer data. The taxpayer compliance can be classified into four classes, which are (1) formal and material compliant taxpayers, (2) formal compliant taxpayers, (3) material compliant taxpayers, and (4) formal and material non-compliant taxpayers. Furthermore, the results of data mining algorithms are compared by using Fuzzy AHP and TOPSIS to determine the best performance classification based on the criteria of Accuracy, F-Score, and Time required. Selection of the taxpayer's priority for more detailed supervision at each level of taxpayer compliance is ranked using Fuzzy AHP and TOPSIS based on criteria of dataset variables. The results show that C4.5 is the best performance classification and achieves preference value of 0.998; whereas the MLP algorithm results from the lowest preference value of 0.131. Alternative taxpayer A233 is the top priority taxpayer with a preference value of 0.433; whereas alternative taxpayer A051 is the lowest priority taxpayer with a preference value of 0.036.

scholarly journals Chronic Disease Prediction Using Effective Feature Selection

2019 ◽  
Vol 8 (2) ◽  
pp. 1211-1216
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Predictive Analytics ◽  
Medical Condition ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Medical Sector ◽  
Build Time ◽  
Life Threatening ◽  
Precautionary Measures

Healthcare is a major sector where there is demand for predictive analytics using machine learning. Healthcare will be largely benefited when useful knowledge can be transferred into timely action to manage hazardous situations in medical sector. Chronic kidney disease is a life threatening disease which can be prevented with timely right predictions and appropriate precautionary measures. In this paper, various machine learning classifiers are applied on the medical dataset to develop a prediction model to tell if a person's present medical condition can lead to the chronic stage of the disease in future. The higher prediction accuracy and decreased build time is obtained with reduced feature set attributes by applying Best First and Greedy stepwise algorithm combined with different classification techniques like Naive Bayes ,Support vector machine (SVM), J48, Random Forest, and K Nearest Neighbor(KNN).

scholarly journals A hybrid cost-sensitive ensemble for heart disease prediction

2020 ◽  
Author(s):  
Zhenya Qi ◽  
Zuoru Zhang
Keyword(s):  
Heart Disease ◽  
Nearest Neighbor ◽  
Statistical Tests ◽  
Disease Diagnosis ◽  
Medical Decision ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Misclassification Cost ◽  
Promising Alternative ◽  
Relief Algorithm

Abstract Background: Heart disease is the primary cause of morbidity and mortality in the world. It includes numerous problems and symptoms. The diagnosis of heart disease is difficult because there are too many factors to analyze. What's more, the misclassification cost could be very high. Methods: A cost-sensitive ensemble method was proposed to improve the efficiency of diagnosis and reduce the misclassification cost. The proposed method contains five heterogeneous classifiers: random forest, logistic regression, support vector machine, extreme learning machine and k-nearest neighbor. T-test was used to investigate if the performance of the ensemble was better than individual classifiers and the contribution of Relief algorithm. Results: The best performance was achieved by the proposed method according to ten-fold cross validation. The statistical tests demonstrated that the performance of the proposed ensemble was significantly superior to individual classifiers, and the efficiency of classification was distinctively improved by Relief algorithm. Conclusions: The proposed ensemble gained significantly better results compared with individual classifiers and previous studies, which implies that it can be used as a promising alternative tool in medical decision making for heart disease diagnosis.

Evaluation of Classification Algorithms vs Knowledge-Based Methods for Differential Diagnosis of Asthma in Iranian Patients

2018 ◽  
Vol 10 (2) ◽  
pp. 22-35 ◽  
Author(s):  
Reza Safdari ◽  
Peyman Rezaei-Hachesu ◽  
Marjan GhaziSaeedi ◽  
Taha Samad-Soltani ◽  
Maryam Zolnoori
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Classification Tree ◽  
Roc Curves ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Knowledge Based ◽  
Class 1 ◽  
Sensitivity Specificity

Medical data mining intends to solve real-world problems in the diagnosis and treatment of diseases. This process applies various techniques and algorithms which have different levels of accuracy and precision. The purpose of this article is to apply data mining techniques to the diagnosis of asthma. Sensitivity, specificity and accuracy of K-nearest neighbor, Support Vector Machine, naive Bayes, Artificial Neural Network, classification tree, CN2 algorithms, and related similar studies were evaluated. ROC curves were plotted to show the performance of the authors' approach. Support vector machine (SVM) algorithms achieved the highest accuracy at 98.59% with a sensitivity of 98.59% and a specificity of 98.61% for class 1. Other algorithms had a range of accuracy greater than 87%. The results show that the authors can accurately diagnose asthma approximately 98% of the time based on demographics and clinical data. The study also has a higher sensitivity when compared to expert and knowledge-based systems.

Prediction of Heart Diseases Using Data Mining Techniques

2018 ◽  
Vol 3 (2) ◽  
pp. 1-9 ◽  
Author(s):  
Nancy Masih ◽  
Sachin Ahuja
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Heart Diseases ◽  
Complete Analysis ◽  
Support Vector ◽  
Classification Algorithms ◽  
Process Data ◽  
Data Mining Techniques ◽  
Healthcare Data ◽  
Hidden Patterns

Health care organizations accumulate large amount of healthcare data, but it is not ‘extracted' to draw hidden patterns which can prove efficient for the decision making process. Data mining techniques can be used to gain insights by discovering hidden patterns which remain undetected manually. Data analytics proves to be useful in detection and identification of the diseases. A complete analysis has been conducted on the FHS (Framingham Heart Study) using various data analytic techniques viz. Decision tree, Naïve Bayes, Support vector machine (SVM) and Artificial neural network (ANN) and the results were ranked according to the accuracy. ANN produce better results than other classification algorithms. The output helps to find out the prominent features that cause heart disease and also identifies the most common features that must be analyzed for prediction of deaths due to heart disease. Despite various studies carried out on heart diseases, the main focus of this study is prediction of heart disease on the dataset of FHS by using various classification algorithms to achieve high accuracy.

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