Prediction of heart diseases using associative classification

Singh, Jagdeep; Kamra, Amit; Singh, Harbhag

doi:10.1109/wecon.2016.7993480

Cited by 32 publications

(11 citation statements)

References 9 publications

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“…The various attributes related to cause of heart diseases are gender, age, chest pain type, blood pressure, blood sugar etc. that can predict early symptoms heart disease [13].…”

Section: International Journal Of Engineering Research and Technology (mentioning

confidence: 99%

Heart Disease Prediction using Machine Learning

Kasabe¹,

Narang²

2020

IJERT

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Data mining is the process of data analyzing from various perspectives and combining it into useful information. This technique is used for finding heart disease. Based on risk factor the heart diseases can be defined very easily. The main aim of this work is to evaluate different classification techniques in heart diagnosis. First, the heart numeric dataset is extracted and preprocess them. After that using extract the features that is condition to be find to be classified by machine learning. Compared to existing; machine learning provides better performance. After classification, performance criteria including accuracy, precision, F-measure is to be calculated. Machine learning provides better performance. The comparison measure expose that Random Forest is the best classifier for the diagnosis of heart disease on the existing dataset.

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“…The various attributes related to cause of heart diseases are gender, age, chest pain type, blood pressure, blood sugar etc. that can predict early symptoms heart disease [13].…”

Section: International Journal Of Engineering Research and Technology (mentioning

confidence: 99%

Heart Disease Prediction using Machine Learning

Kasabe¹,

Narang²

2020

IJERT

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“…In fact, associative classifiers have not received much attention in WSN-based smart homes wellness determination, due to their tendency to generate a large number of rules. However, associative classifiers have been proved to be potential candidates in other healthcare domains for their high accuracy, as compared to other classifiers [30,31]. Associative classifiers provide high accuracy; during testing phases a global best approach is followed to identify the appropriate classification rule for the test instance, while other classifiers (such as decision trees) follow a greedy approach (local best), as reported in [17].…”

Section: Related Workmentioning

confidence: 99%

A Context-Aware Accurate Wellness Determination (CAAWD) Model for Elderly People Using Lazy Associative Classification

Ujager

Mahmood

2019

Sensors

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Wireless Sensor Network (WSN) based smart homes are proving to be an ideal candidate to provide better healthcare facilities to elderly people in their living areas. Several currently proposed techniques have implementation and usage complexities (such as wearable devices and the charging of these devices) which make these proposed techniques less acceptable for elderly people, while the behavioral analysis based on visual techniques lacks privacy. In this paper, a context-aware accurate wellness determination (CAAWD) model for elderly people is presented, where behavior monitoring information is extracted by using simple sensor nodes attached to household objects and appliances for the analysis of daily, frequent behavior patterns of elderly people in a simple and non-obtrusive manner. A contextual data extraction algorithm (CDEA) is proposed for the generation of contextually comprehensive behavior-training instances for accurate wellness classification. The CDEA presents an activity’s spatial–temporal information along with behavioral contextual correlation aspects (such as the object/appliance of usage and sub-activities of an activity) which are vital for accurate wellness analysis and determination. As a result, the classifier is trained in a more logical manner in the context of behavior parameters which are more relevant for wellness determination. The frequent behavioral patterns are classified using the lazy associative classifier (LAC) for wellness determination. The associative nature of LAC helps to integrate spatial–temporal and related contextual attributes (provided by CDEA) of elderly behavior to generate behavior-focused classification rules. Similarly, LAC provides high accuracy with less training time of the classifier, includes minimum-support behavior patterns, and selects highly accurate classification rules for the classification of a test instance. CAAWD further introduces the ability to contextually validate the authenticity of the already classified instance by taking behavioral contextual information (of the elderly person) from the caregiver. Due to the consideration of spatial–temporal behavior contextual attributes, the use of an efficient classifier, and the ability to contextually validate the classified instances, it has been observed that the CAAWD model out-performs currently proposed techniques in terms of accuracy, precision, and f-measure.

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“…Authors proposed a hybrid approach with two association rules methods, FP-Growth technique and Apriori method to predict heart diseases [10]. They used OneR, NB, KNN, J48 and ZeroR classifiers on Cleveland dataset with 14 attributes.…”

Section: Related Workmentioning

confidence: 99%

Heart Disease Prediction using Ensemble Methods

Deshmukh*¹

2019

IJRTE

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Nowadays, people are suffering from many health issues. One of them is heart disease among the worldwide population. This causes due to imbalance lifestyle and unhealthy food consumption. The data generated by hospitals is huge and complex by nature which store patients medical and demographic information. Accurate and prompt diagnosis of heart diseases are becoming more challenging task in medical domain due to the complex data. Therefore, the computer aided systems are useful to store this complex and multivariate data to generate useful decisions. Machine learning techniques are used to classify and to predict the diseases. In this study, Majority voting classifier and Bagging ensemble method both have been evaluated. These ensemble methods combined the five base classifiers including DT (Decision Tree), LR (Logistic Regression), ANN (Artificial Neural Network), NB (Naïve Bayesian), and KNN (K-Nearest Neighbour). Bagging ensemble approach is used to combine the multiple classifiers prediction abilities for better performance. Experimental work is performed on Cleveland dataset using 14 attributes which is available online on UCI Repository. The results showed that the Bagging ensemble method is performed better to achieve higher accuracy of 87.78 %.

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Prediction of heart diseases using associative classification

Cited by 32 publications

References 9 publications

Heart Disease Prediction using Machine Learning

Heart Disease Prediction using Machine Learning

A Context-Aware Accurate Wellness Determination (CAAWD) Model for Elderly People Using Lazy Associative Classification

Heart Disease Prediction using Ensemble Methods

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