## 2 Related Work
In [24], the mortality of patients from heart failure was detected via ML algorithms. The authors proposed a stacking model that outperformed all the base models, such as RF. RF achieved an accuracy of 88.89%, while the stacking model gave 90% accuracy and performed the best on Heart Failure Prediction dataset. [PERSON] et al. [25] proposed a model comprising a DNN and a \(X^{2}\) statistical model. The DNN was used for classification, while the \(X^{2}\) statistical model was used for feature extraction. The proposed model was applied to the Cleveland dataset. The proposed model beat ANN, with an accuracy of 93.33%. In [26], the authors proposed a method for Chronic Heart Failure (CHF) based on heart sounds. The proposed method was composed of ML and DL. DL learned from signal temporal representation and ML learned from important features. This method was applied to the CHF dataset and scored an accuracy of 89.3%, which beats the base model by 9.1%. In [27], the authors introduced a DL approach to predict heart failure patients with high risk. LSTM memory layers were utilized in the proposed model. For comparison, different ML techniques, such as LR, RF, and XGBoost were used. The results showed that the proposed model outperformed all the base ML techniques with an AUC of 0.861. In [28], the authors proposed a traditional LR model to predict heart failure. After training and testing, the outcomes were modeled with LR and compared with DL models and a Gradient Boosting Model using sequential and non-sequential inputs. It turned out that the proposed DL models outperformed traditional LR. [PERSON] et al. [29] proposed DL models such as LSTM and CNN, for automatic detection of arrhythmia for IoT application. The images were obtained from ECG signals, represented in a two-dimensional format and fed into DL models for classification. The proposed model in case of noisy data were found to be efficient and robust. In [30], the authors introduced a framework comprising SVM, DT, and RF for diabetes prediction. The authors named their proposed model the Intelligent Diabetes Mellitus Prediction Framework (IDMPF). Using this model, the authors described different assessment strategies, training procedures, and issues in predicting diabetes. With an accuracy rate of 83%, the suggested model performed the best. [PERSON]. et al. [31] proposed using a DL model, CNN, and an ML technique, KNN. CNN was used for accurate disease prediction and feature extraction while KNN was used to find an accurate match in the dataset and predict the result. The proposed model's performance comparison was performed comparing GNB, LR, and DT. Since the proposed model had a 97% accuracy rate, it was deemed to be the best. [PERSON] et al. [32] introduced ensemble learning techniques to predict Parkinson disease. The proposed model beat all the traditional ML models, such as SVM, KNN, RF, DT, Multilayer Perceptron (MLP), Stacking Classifier (SC), and LR. The proposed model outperformed all the models, yielding 94.87% accuracy. In [33], the authors proposed a model to predict diabetes in an individual. After sorting out the complete dataset, the authors used different DL models to predict diabetes. They compared different models such as two-class decision jungle, two-class LR, two-class boosted DT, as well as two-class NN. The two-class boosted model turned out to perform the best and provided accuracy of 99%, beating the other two models. Table 4 presents the summarized related work.