# AI-Enabled Electrocardiogram Analysis for Disease Diagnosis
## 4 Use of ECG with AI for Clinical Analysis and Diagnosis
### Use of ML with ECG for Heart Health Diagnosis

Table 2: _Cont._A framework for machine learning using ECG data for disease diagnosis is depicted in Figure 4. The process of using ECG data with machine learning (ML) for illness diagnosis is shown in the flow diagram. The first step is gathering ECG data in many formats, including tabular, waveform, picture, and video. The data are then split into two sets to train and assess the ML model: training and testing datasets. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Ref. ** & **Year** & **Hear Disease** & **Algorithm** & **Summary** & **Limitations** \\ \hline \multirow{9}{*}{[79]} & \multirow{9}{*}{2020} & \multirow{9}{*}{Arrhythmia} & \multirow{9}{*}{Support vector machine} & ECG data are used in the study to assess heart abnormalities that cause arrhythmias. After filtering and segmenting, the R-R interval, BPM, P wave, and QRS peak are extracted. SVM classifier validates the technique’s efficacy with 91\% accuracy, improved precision, recall, and F1 score. & Noisy data: The study did not consider the impact of noise and artifacts on the ECG signals. Single classifier: The study only used a single machine learning classifier, which may not be classifier, which may not be the best approach for all arrhythmias. \\ \hline \multirow{9}{*}{[80]} & \multirow{9}{*}{2020} & \multirow{9}{*}{Myocardial Infarction} & \multirow{9}{*}{RF, SVM, KNN, and NN} & The study proposes a new ECG-based MI detection method. Features that were extracted using PCA, statistics, entropy, and data augmentation. In terms of accuracy, sensitivity, specificity, and F1, random forests perform better than SVM, BPNN, and KNN. & Single lead: The study only used a small dataset of 200 patients, which may not represent the wider population. Limited features: The study only considered a limited number of features, such as ECG signals and medical records. \\ \hline \multirow{9}{*}{[81]} & \multirow{9}{*}{2020} & \multirow{9}{*}{CVD} & \multirow{9}{*}{Support vector machine} & ECG fragment alignment and PCA classification method are introduced. & Small dataset: The study used a small dataset of 200 patients, which may not represent the wider population. \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline  
The next step is to create and train prediction models using various ML algorithms on the training dataset. In order to compare various models and improve the ML algorithms, the assessment process is carried out iteratively. The goal is to find the model that performs the best and most correctly forecasts the target illness. The flow diagram moves to the final model selection phase after the assessment. The final illness prediction model is selected based on its performance and accuracy. This model may be ready for application and captures the discovered correlations and patterns in the ECG data. This section aims to divert attention away from a comparison based on high accuracy or other performance indicators to the extent of ongoing research efforts in utilizing machine learning algorithms to diagnose heart diseases. While comparative analysis is frequently used to assess the efficacy of various algorithms or methodologies, it may not necessarily be the best strategy for applying machine learning to diagnose cardiac conditions. The reasons behind avoiding performance metrics are the following: first, there is no generalized protocol to evaluate a machine learning model while using ECG for heart disease diagnosis. Second, there is no standardized dataset, and people use different datasets for evaluation, making it hard to compare different models. The quantity, diversity, quality, and inclusion standards of the heart disease datasets utilized in various research may differ. Because of these variances, comparing machine learning algorithms conclusively using only accuracy or other criteria can be challenging.