Heart Disease Prediction Using Decision Tree and SVM

Saraswathi, R. Vijaya; Gajavelly, Kovid; Nikath, A. Kousar; Vasavi, R.; Anumasula, Rakshith Reddy

doi:10.1007/978-981-16-7389-4_7

Cited by 9 publications

(3 citation statements)

References 18 publications

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“…It builds decision nodes in which the internal nodes represent data characteristics, the edges represent decision rules, and each leaf node represents the class label, i.e., the target class label is specified, if that defined path is taken. The step begins with the root node of the decision tree; subsequent nodes are picked, and the process continues until the lead node is reached; the class label at the leaf node is the predicted class label [13]. Fig.…”

Section: Decision Treesmentioning

confidence: 99%

Diagnosis of Heart Disease Using IoT in Edge with Multiple Machine Learning Techniques

2023

Recent Trends in Data Science and Its Applications

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Edge computing bridges the gap between industrial clouds and field devices for the Industrial Internet of Things (IoT). The most difficult aspect of edge computing is gathering data from different devices. The role of Edge is to process client's data at the network's edge, closer to the source. Due to the rapid increase in data traffic exchanged worldwide, there is an increasing need to collect and process data from sensors and devices of the IoT, which are operated in real time from remote locations and hostile operating environments. The proposed system aims to develop an edgebased heart disease prediction. The system was developed using a simple microcontroller that integrates temperature, heart rate and blood pressure. The measured value is tested with machine learning model to understand the patient's condition. Many feature combinations are used to reveal the prediction model and also for large number of well-known machine learning classification algorithms. Prediction model achieved the accuracy measure of 86% for SVM,92.24% for Decision Tree Classifier, 93.01% for Random Forest and 86.91% for K-Nearest Neighbor. So, from these four algorithms, Decision Tree Classifier gives the superior prediction, satisfying all the measures of precision, recall, accuracy and F-Score.

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Section: Decision Treesmentioning

confidence: 99%

Diagnosis of Heart Disease Using IoT in Edge with Multiple Machine Learning Techniques

2023

Recent Trends in Data Science and Its Applications

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“…The assigned class label at the terminal node corresponds to the anticipated class label. (Saraswathi, et al, 2022) Information gain is the measurement of changes in entropy after the segmentation of a dataset based on an attribute. It calculates how much information a feature provides about a class.…”

Section: Decision Tree Classifiermentioning

confidence: 99%

Heart Disease Prediction Using Outlier Removal based Max Voting Ensemble Method

Chapagain

2023

Int. J. Engin. Technol.

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Heart disease has emerged as a serious health concern for many individuals due to its high death rate around the world. The routine clinical data analysis has a significant difficulty in the early diagnosis of cardiac disease. The identification of cardiac disease may benefit from the use of machine learning. To improve machine learning models, several studies have previously been conducted. The suggested study uses the maximum voting ensemble technique of classification to effectively identify heart disease. The suggested classifier is a more reliable and accurate approach. To identify and eliminate outliers, conduct Inter quartile range outlier removal and min-max normalization during preprocessing. Accuracy, Precision, Recall, and F1 Score are calculated and evaluated against various models. For the heart disease dataset collected from the Kaggle, the suggested max voting ensemble classifier has an accuracy of 99.22%.

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“…Artificial neural networks can handle various classification problems, and convolutional neural networks in deep learning are also used to extract phenotypes and make risk predictions [6]. Support vector machines [7], decision trees [8], and random forests [9], are also widely used methods. In recent years, these algorithms have been used to structure model and predict risks.…”

Section: Introductionmentioning

confidence: 99%

High-Risk HPV Cervical Lesion Potential Correlations Mining over Large-Scale Knowledge Graphs

Zhou,

Xu,

Wang

et al. 2024

Applied Sciences

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Lesion prediction, a very important aspect of cancer disease prediction, is an important marker for patients before they become cancerous. Currently, traditional machine learning methods are gradually applied in disease prediction based on patient vital signs data. Accurate prediction requires a large amount and high quality of data, however, the difficulty in obtaining and incompleteness of electronic medical record (EMR) data leads to certain difficulties in disease prediction by traditional machine learning methods. Secondly, there are many factors that contribute to the development of cervical lesions, some risk factors are directly related to it while others are indirectly related to them. In addition, risk factors have an interactive effect on the development of cervical lesions; it does not occur in isolation, a large-scale knowledge graph is constructed base on the close relationships among risk factors in the literature, and new potential key risk factors are mined based on common risk factors through a subgraph mining method. Then lesion prediction algorithm is proposed to predict the likelihood of lesions in patients base on the set of key risk factors. Experimental results show that the circumvents the problems of large number of missing values in EMR data and discovered key risk factors that are easily ignored but have better prediction effect. Therefore, The method had better accuracy in predicting cervical lesions.

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Heart Disease Prediction Using Decision Tree and SVM

Cited by 9 publications

References 18 publications

Diagnosis of Heart Disease Using IoT in Edge with Multiple Machine Learning Techniques

Diagnosis of Heart Disease Using IoT in Edge with Multiple Machine Learning Techniques

Heart Disease Prediction Using Outlier Removal based Max Voting Ensemble Method

High-Risk HPV Cervical Lesion Potential Correlations Mining over Large-Scale Knowledge Graphs

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