Heart Disease Diagnosis Using Machine Learning Algorithm

Ghumbre, Shashikant; Ghatol, Ashok A.

doi:10.1007/978-3-642-27443-5_25

Cited by 53 publications

(17 citation statements)

References 16 publications

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“…Experimental setting. We mount our backdoor attack for the mortality prediction task agnostically, and evaluate its effectiveness against Logistic Regression (LR), Multi Layer Perceptron (MLP), and Long-Short Term Memory (LSTM) as representative machine learning algorithms for EHRs in the literature (Harutyunyan et al 2019;Johnson et al 2016;Lipton et al 2015;Ghumbre and Ghatol 2012).…”

Section: Resultsmentioning

confidence: 99%

Machine Learning with Electronic Health Records is vulnerable to Backdoor Trigger Attacks

Joe¹,

Mehra²,

Shin³

et al. 2021

Preprint

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Electronic Health Records (EHRs) provide a wealth of information for machine learning algorithms to predict the patient outcome from the data including diagnostic information, vital signals, lab tests, drug administration, and demographic information. Machine learning models can be built, for example, to evaluate patients based on their predicted mortality or morbidity and to predict required resources for efficient resource management in hospitals. In this paper, we demonstrate that an attacker can manipulate the machine learning predictions with EHRs easily and selectively at test time by backdoor attacks with the poisoned training data. Furthermore, the poison we create has statistically similar features to the original data making it hard to detect, and can also attack multiple machine learning models without any knowledge of the models. With less than 5% of the raw EHR data poisoned, we achieve average attack success rates of 97% on mortality prediction tasks with MIMIC-III database against Logistic Regression, Multilayer Perceptron, and Long Short-term Memory models simultaneously.

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Section: Resultsmentioning

confidence: 99%

Machine Learning with Electronic Health Records is vulnerable to Backdoor Trigger Attacks

Joe¹,

Mehra²,

Shin³

et al. 2021

Preprint

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“…e heart disease patient's characteristics are easily identified by the Naïve Bayes classifier technique. is algorithm will find the input attributes' probability during a predictable disease state [18]. Figure 2 shows ML techniques for heart disease prediction.…”

Section: Naïve Bayes Weighted Approach (Nbwa)mentioning

confidence: 99%

Machine Learning Technology-Based Heart Disease Detection Models

Nagavelli

Samanta

Chakraborty

2022

Journal of Healthcare Engineering

104

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At present, a multifaceted clinical disease known as heart failure disease can affect a greater number of people in the world. In the early stages, to evaluate and diagnose the disease of heart failure, cardiac centers and hospitals are heavily based on ECG. The ECG can be considered as a regular tool. Heart disease early detection is a critical concern in healthcare services (HCS). This paper presents the different machine learning technologies based on heart disease detection brief analysis. Firstly, Naïve Bayes with a weighted approach is used for predicting heart disease. The second one, according to the features of frequency domain, time domain, and information theory, is automatic and analyze ischemic heart disease localization/detection. Two classifiers such as support vector machine (SVM) with XGBoost with the best performance are selected for the classification in this method. The third one is the heart failure automatic identification method by using an improved SVM based on the duality optimization scheme also analyzed. Finally, for a clinical decision support system (CDSS), an effective heart disease prediction model (HDPM) is used, which includes density-based spatial clustering of applications with noise (DBSCAN) for outlier detection and elimination, a hybrid synthetic minority over-sampling technique-edited nearest neighbor (SMOTE-ENN) for balancing the training data distribution, and XGBoost for heart disease prediction. Machine learning can be applied in the medical industry for disease diagnosis, detection, and prediction. The major purpose of this paper is to give clinicians a tool to help them diagnose heart problems early on. As a result, it will be easier to treat patients effectively and avoid serious repercussions. This study uses XGBoost to test alternative decision tree classification algorithms in the hopes of improving the accuracy of heart disease diagnosis. In terms of precision, accuracy, f1-measure, and recall as performance parameters above mentioned, four types of machine learning (ML) models are compared.

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“…2 [23]. (2) where lrd() is the local reachability density of a given point with respect to MinPts, and is the list of nearest MinPts to the point p given in Equ. Z.…”

Section: Local Outlier Factormentioning

confidence: 99%

“…The diverse applications of classification algorithms encouraged researchers to enhance the performance of these algorithms; these applications include customer target marketing [1], medical disease diagnosis [2], supervised event detection [3], multimedia data analysis [4], biological data analysis [5], document categorization and filtering [6], and social network analysis [7]. However, enhancing classifiers performance is a challenging mission.…”

Section: Introductionmentioning

confidence: 99%

On the Enhancement of Classification Algorithms Using Biased Samples

Al‐Mamory

2019

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Classification algorithms' performance could be enhanced by selecting many representative points to be included in the training sample. In this paper, a new border and rare biased sampling (BRBS) scheme is proposed by assigning each point in the dataset an importance factor. The importance factor of border points and rare points (i.e. points belong to rare classes) is higher than other points. Then the points are selected to be in the training sample depending on these factors. Including these points in the training sample enhances classifiers experience. The results of experiments on 10 UCI machine learning repository datasets prove that the BRBS algorithm outperforms many sampling algorithms and enhanced the performance of several classification algorithms by about 8%. BRBS is proposed to be easy to configure, covering all points space, and generate a unique samples every time it is executed.

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Heart Disease Diagnosis Using Machine Learning Algorithm

Cited by 53 publications

References 16 publications

Machine Learning with Electronic Health Records is vulnerable to Backdoor Trigger Attacks

Machine Learning with Electronic Health Records is vulnerable to Backdoor Trigger Attacks

Machine Learning Technology-Based Heart Disease Detection Models

On the Enhancement of Classification Algorithms Using Biased Samples

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