HEART FAILURE DETECTION IN A SINGLE HEARTBEAT USING AI

Written by: Bhargav D J (1st year MCA)

ABSTRACT

Artificial Intelligence (AI) performs human intelligence-dependent tasks using tools such as Machine Learning, and its subtype Deep Learning. AI has incorporated itself in the field of cardiovascular- lar medicine, and increasingly employed to revolutionize diagnosis, treatment, risk prediction, clinical care, and drug discovery. Heart failure has a high prevalence, and mortality rate following hospital- inaction being 10.4% at 30- days, 22% at 1-year, and 42.3% at 5-years. Early detection of heart failure is of vital importance in shaping the medical, and surgical interventions specific to HF patients. This has been accomplished with the advent of Neural Network (NN) model, the accuracy of which has proven to be 85%. AI can be of tremendous help in analyzing raw image data from cardiac imaging tech niques (such as echocardiography, computed tomography, cardiac MRI amongst others) and electrocardiogram recordings through in- corporation of an algorithm. The use of decision trees by Rough Sets (RS), and logistic regression (LR) methods utilized to construct decision-making model to diagnose congestive heart failure, and role of AI in early detection of future mortality and destabilization episodes has played a vital role in optimizing cardiovascular disease outcomes. The review highlights the major achievements of AI in re- cent years that has radically changed nearly all areas of HF prevention, diagnosis, and management.

KEYWORDS - Deep learning; Decision trees; Heart failure; Artificial neural network; Electronic health records; Echocardiography; Mobile health

INTRODUCTION

Artificial Intelligence (AI) possesses the capability to per- form human intelligence dependent tasks such as receiving perspicuity, learning semantics, and formulating an analysis using various algorithms and cognitive computing . AI uses the concept of Learning, which can be classified into supervised, unsupervised, and re- enforcement. Machine Learning (ML) is the core of AI that uses a model based on training data to make decisions, and program algorithms to solve the problem . The commonly utilized classification models include Binary, Multi-class, Multi-label and Imbalanced Classification. Binary classification uses algorithms like Logistic Regression, k-nearest neighbors, decisions tree, support vector machine and naïve Bayes to classify two labels’ tasks. Multi-class uses algorithms like decisions tree, support vector machine, naïve Bayes, random forest, and gradient boosting to classify tasks involving more than two la- bels. Multi-label classifies tasks that have two or more class labels, where one or more class labels may be predicted for each example, unlike the multi-class where a single class label is predicted for each example. The class labels with unequally distributed tasks are classified using the Imbalanced classification model . The distributions can vary from slightly imbalanced to severely imbalanced. It constitutes a significant challenge in predictive modelling as algorithms used for imbalanced classification are based on assumptions . Class labels are often string values, e.g., “spam”, “not spam”, which are mapped to numeric values in the process of label encoding . Deep Learning (DL) is a class of the ML algorithm that uses higher level features such as neural networks derived from a model of the human brain which allows a computer system to read, build, and learn complex hierarchical representation .

METHODOLOGY

Heart disease is becoming more prevalent for several reasons. Early detection of heart disease is essential for starting treatment. To fulfill this requirement, the strategy mentioned in this study discusses various ML techniques that will enable everyone to become aware of their risk early.

ECHOCARDIOGRAPHY

All images were obtained using a standard ultrasound machine with a 2.5- MHz probe. Standard techniques were used to obtain M-mode, two- dimensional, and Doppler measurements in accordance with the American Society of Echocardiography guidelines 20 . Tissue-Doppler-derived peak systolic, early, and late diastolic velocities of the septal mitral annulus were recorded. Left ventricular end-systolic and end-diastolic volumes were measured from apical four- and two-chamber views and LVEF was calculated using Simpson’s biplane method. Study variables HF was defined when patients had signs or symptoms of HF and either lung congestion, objective findings of LV systolic dysfunction, or structural heart disease. The diagnosis of HF was confirmed by two independent HF specialists who had >10 years of clinical experience. The diagnosis by the experts was considered the gold standard.According to the LVEF on echocardiography, patients were classified as having HFrEF (LVEF < 40%), HFmrEF (40% ≤ LVEF & lt ; 50%), and HFpEF (LVEF ≥ 50%). The diagnostic accuracy of AI-CDSS was measured using experts’ diagnosis as the gold standard. Concordance was defined as present when experts and AI-CDSS had the same diagnosis, i.e., both HF or both no-HF. Discordance was defined to exist when there was a disagreement between diagnoses.

STATISTICAL ANALYSIS

Descriptive statistics were calculated to determine the clinical character- istics and outcomes of the registry population. Data were presented as numbers and frequencies for categorical variables and as mean ± standard deviation or median with interquartile range for continuous variables. For the comparison between groups, the χ 2 test (or Fisher’s exact test when any expected cell count was <5 for a 2 × 2 table) was used for categorical variables, whereas unpaired Student’s t-test was used for continuous variables. Concordance was expressed as the percentage agreement.

a. Logistic regression  - Logistic regression models are used for binary classification tasks in heart failure detection.

1. Input: feature selected data

2. Output: best classification

3. Algorithms:

4. For i <- 1 to k

5. For each training & testing data instance di :

6. Set the target value for the regression to Z = yi – p ( 1 – dj ) / [ p ( 1- dj ) * ( 1 –p(1 – dj ))]

7. Initialize the weight of instance dj to p(1/dj)*(1-p)*(1/dj)

8. Finalize a f(j) to the data with class value (zj) & weight (wj) classification label; decision

9. Assign ( class label : 1) if p(1/dj)>0.5 , otherwise ( class label : 2)

b. Bayesian networks - Bayesian networks are used to model the probabilities relationships between various clinical features and the likelihood of heart failure 

c. K-nearest neighbor  - K – NN algorithms classify [patients based on their similarity to other patients with known heart failure outcomes.

d. Decision tree - Decision trees can be used to create simple rules for heart failure prediction based on patient data analysis.

e. Random forest  - Random forest is an ensemble learning technique that combines multiple decision trees.it can be used to predict heart failure risk by considering a range of patient variables.

f. Neural networks - Deep learning techniques , including artificial neural networks , are used for heart failure detection . convolutional neural networks (CNNS)may be employed for image analysis while recurrent neural networks(RNNs)can be used for time-series data such as electrocardiograms.

CONCLUSION

This research has provided a comprehensive study of patient characteristics for heart disease prediction. Correlation-based Feature Subset Selection method with the Best First Search has been carried out to select the most significant features. It has been discovered that all of the features are not strongly connected and that a combination of just 14 features (age, gender, smoking, obesity, diet, physical activity, stress, chest pain type, previous chest pain, blood pressure diastolic, diabetes, troponin, ECG, and target) significantly contribute to the prediction of heart disease. Finally, the datasets containing all features and selected features are used to develop seven AI (logistic regression, Naïve Bayes, K-NN, SVM, decision tree, random forest, and MLP) methods. The accuracy rate of Random Forest utilizing selected attributes is 90%, coupled with 90.91% precision, 100% recall, 90.91% F1-score, and 89.90% ROC-AUC score, which is the highest performance rate when compared to other AI techniques. The dataset of selected features outperforms the dataset of all features, excluding Naïve Bayes. The lack of extra discriminatory feature sets and additional datasets has drastically decreased the performance of the Naïve Bayes model. It has been noticed that most of the dataset's features are strongly associated with one another. The clinicians will be helped in proficiently archiving the records by systematically studying the efficiency of the various features. The data management team can archive only the features crucial for predicting heart disease, as opposed to recording and preserving all the features. As part of our next effort, we want to validate our suggested methodology externally.