scholarly journals Prediction of Heart Diseases through Artificial Intelligence and Data Mining

2019 ◽  
Vol 13 (2) ◽  
pp. 17-24 ◽  
Author(s):  
A V S Pavan Kumar ◽  
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Heart Diseases

Sentiment analysis of students in ideological and political teaching based on artificial intelligence and data mining

2021 ◽  
pp. 1-10
Author(s):  
Wan Hongmei ◽  
Tang Songlin
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Sentiment Analysis ◽  
Machine Learning Algorithms ◽  
Classroom Teaching ◽  
Functional Modules ◽  
Semantic Ambiguity ◽  
Calculation Methods ◽  
Multiple Factors ◽  
Quantitative Calculation

In order to improve the efficiency of sentiment analysis of students in ideological and political classrooms, under the guidance of artificial intelligence ideas, this paper combines data mining and machine learning algorithms to improve and propose a method for quantifying the semantic ambiguity of sentiment words. Moreover, this paper designs different quantitative calculation methods of sentiment polarity intensity, and constructs video image sentiment recognition, text sentiment recognition, and speech sentiment recognition functional modules to obtain a combined sentiment recognition model. In addition, this article studies student emotions in ideological and political classrooms from the perspective of multimodal transfer learning, and optimizes the deep representation of images and texts and their corresponding deep networks through single-depth discriminative correlation analysis. Finally, this paper designs experiments to verify the model effect from two perspectives of single factor sentiment analysis and multi-factor sentiment analysis. The research results show that comprehensive analysis of multiple factors can effectively improve the effect of sentiment analysis of students in ideological and political classrooms, and enhance the effect of ideological and political classroom teaching.

Improved differentiation classification of variable precision artificial intelligence higher education management

2021 ◽  
pp. 1-10
Author(s):  
Chao Dong ◽  
Yan Guo
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Data Mining ◽  
Decision Tree ◽  
Classification Accuracy ◽  
Attribute Selection ◽  
Higher Education Management ◽  
Education Management ◽  
Decision Tree Classification

The wide application of artificial intelligence technology in various fields has accelerated the pace of people exploring the hidden information behind large amounts of data. People hope to use data mining methods to conduct effective research on higher education management, and decision tree classification algorithm as a data analysis method in data mining technology, high-precision classification accuracy, intuitive decision results, and high generalization ability make it become a more ideal method of higher education management. Aiming at the sensitivity of data processing and decision tree classification to noisy data, this paper proposes corresponding improvements, and proposes a variable precision rough set attribute selection standard based on scale function, which considers both the weighted approximation accuracy and attribute value of the attribute. The number improves the anti-interference ability of noise data, reduces the bias in attribute selection, and improves the classification accuracy. At the same time, the suppression factor threshold, support and confidence are introduced in the tree pre-pruning process, which simplifies the tree structure. The comparative experiments on standard data sets show that the improved algorithm proposed in this paper is better than other decision tree algorithms and can effectively realize the differentiated classification of higher education management.

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 Cardiovascular Disease Prediction using Data Mining Techniques

2017 ◽  
Vol 10 (2) ◽  
pp. 520-528 ◽  
Author(s):  
Mudasir Kirmani
Keyword(s):  
Data Mining ◽  
Cardiovascular Disease ◽  
Cardiovascular Diseases ◽  
High Risk ◽  
Learning Algorithm ◽  
Heart Diseases ◽  
Circulatory System ◽  
World Health ◽  
Support Vector ◽  
Predictive Tool

Cardiovascular disease represents various diseases associated with heart, lymphatic system and circulatory system of human body. World Health Organisation (WHO) has reported that cardiovascular diseases have high mortality rate and high risk to cause various disabilities. Most prevalent causes for cardiovascular diseases are behavioural and food habits like tobacco intake, unhealthy diet and obesity, physical inactivity, ageing and addiction to drugs and alcohol are to name few. Factors such as hypertension, diabetes, hyperlipidemia, Stress and other ailments are at high risk to cardiovascular diseases. There have been different techniques to predict the prevalence of cardiovascular diseases in general and heart disease in particular from time to time by implementing variety of algorithms. Detection and management of cardiovascular diseases can be achieved by using computer based predictive tool in data mining. By implementing data mining based techniques there is scope for better and reliable prediction and diagnosis of heart diseases. In this study we studied various available techniques like decision Tree and its variants, Naive Bayes, Neural Networks, Support Vector Machine, Fuzzy Rules, Genetic Algorithms, and Ant Colony Optimization to name few. The observations illustrated that it is difficult to name a single machine learning algorithm for the diagnosis and prognosis of CVD. The study further contemplates on the behaviour, selection and number of factors required for efficient prediction.

scholarly journals Predicting heart ailment in patients with varying number of features using data mining techniques

2019 ◽  
Vol 8 (1) ◽  
pp. 56
Author(s):  
T R Stella Mary ◽  
Shoney Sebastian
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Random Forest ◽  
Naive Bayes ◽  
Heart Diseases ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Data Mining Techniques ◽  
Using Data ◽  
Almost All

<span>Data mining can be defined as a process of extracting unknown, verifiable and possibly helpful data from information. Among the various ailments, heart ailment is one of the primary reason behind death of individuals around the globe, hence in order to curb this, a detailed analysis is done using Data Mining. Many a times we limit ourselves with minimal attributes that are required to predict a patient with heart disease. By doing so we are missing on a lot of important attributes that are main causes for heart diseases. Hence, this research aims at considering almost all the important features affecting heart disease and performs the analysis step by step with minimal to maximum set of attributes using Data Mining techniques to predict heart ailments. The various classification methods used are Naïve Bayes classifier, Random Forest and Random Tree which are applied on three datasets with different number of attributes but with a common class label. From the analysis performed, it shows that there is a gradual increase in prediction accuracies with the increase in the attributes irrespective of the classifiers used and Naïve Bayes and Random Forest algorithms comparatively outperforms with these sets of data.</span>

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