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We calculate heart disease by using artificial intelligence through machine learning techniques to find patterns in data that is connected to heart health. So, we aim for a well improved diagnosis, forecasting and treatment. The basic outline of the process has been explained.
· Data Collection:
The following are the necessary data that has to collected related to heart health. The patients age, gender etc. Collect the previous medical history of the patient Hypertension, diabetes, previous heart conditions, Cholesterol levels, blood pressure readings, etc. Get to know the basic lifestyle of the patient such as Smoking, alcohol consumption, physical activity, etc. Identify the patterns and anomalies in electrical activity that is Electrocardiogram (ECG) Data. Gather the Imaging Data – Echocardiograms, angiograms, MRIs, etc.
· Data Preprocessing:
At this step we can remove any irregularities, outliers or the unrelated information and cleanse the data. Make sure the data is on a consistent scale. Feature Extraction should be done mainly for cases like ECG data where raw signals need to be transformed. the robustness of the model can be improved. Using techniques like imputation the data that is missing can be well handled.
- Feature Selection/Engineering:
The most important variables or structures that contribute to the result must be identified. For a better correlation with heart disease the related features must be combined to get a new one.
- Model Selection:
Various ML algorithms can be used. Under supervised learning various algorithms like Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, Neural Networks, Support Vector Machines, etc will be used. For clustering or segmenting patient groups unsupervised learning is useful. Convolutional Neural Networks (CNNs) for imaging data, and Recurrent Neural Networks (RNNs) or 1D-CNNs for ECG sequences under deep learning is referred.
- Training:
The dataset must be divided into a training set and a test set. Train the selected model on the training dataset.
- Validation & Testing:
Confirmation of the model’s performance by using techniques like cross-validation. The model on unseen data should be verified to estimate the prediction capabilities.
- Model Evaluation Metrics:
Accuracy, precision, recall, F1-score, and AUC-ROC curve are the metrics that are commonly used. We must note that a high level of accuracy does not specify the success of a model. In the background of a medical scenario the presence of negatives will lead to significant risk and a negative result.
- Deployment:
Once a model is accepted it must be applied within a clinical atmosphere and combined into electronic health records or applied in wearable devices, thereby helping healthcare experts.
- Continuous Learning:
The model must be taken to improve accuracy so that the data becomes accessible.
· Ethical Considerations:
The decisions that AI takes should be clear. In order to avoid biases the data must be diverse and illustrative. Data security of the patients must be maintained. AI should act as a support and not take the role of a clinical judgement.
Key Takeaways:
- AI uses has been found to improve the precision of heart disease calculation and make more easier to sense at an early stage.
- So, by combining various types of data, as demographic information and ECG readings, a complete understanding can be attained.
- It’s significant to identify the limitations and uncertainties that are related by using AI calculations in medical applications.
- Joint effort of, data scientists and cardiologists can make the most of the success of these AI models.
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HEART DISEASE PREDICTION USING ARTIFICIAL INTELLIGENCE THESIS TOPICS
- An artificial intelligence model for heart disease detection using machine learning algorithms
Keywords
Artificial intelligence, heart disease detection system, Machine learning, Predictive analytics, Random Forest classifier algorithm
Implementation plan
Step 1: Initially we load the input images from a database that involves data of the patients, which are age, sex, chol, treetops, and many more.
Step 2: Next we apply the Pre-processing for performing logistic regression process, and evaluating the dataset’s attributes.
Step 3: Next we perform the Features extraction step; in this Step we will implement decision tree algorithm to give the high performance. Feature extraction helps to reduce the amount of redundant data from the data set.
Step 4: Next we perform the Classification step; A random forest classifier algorithm is developed to identify heart diseases with higher accuracy.
Step 5: The performance of these work is measured through the following performance metrics, Accuracy, Precision, Recall and F-Score.
Software Requirement: Python – 3.9.6 and Operating System: Windows 10(64-bit)
- Heart Disease Prediction using Artificial Intelligence
Keywords
Artificial intelligence, heart disease detection system, Machine learning, Predictive analytics, Random Forest classifier algorithm
Implementation plan
Step 1: Initially we load the input images from a database that involves data of the patients, which are age, sex, chol, treetops, and many more.
Step 2: Next we apply the Pre-processing for performing logistic regression process, and evaluating the dataset’s attributes.
Step 3: Next we perform the Features extraction step; in this Step we will implement decision tree algorithm to give the high performance. Feature extraction helps to reduce the amount of redundant data from the data set.
Step 4: Next we perform the Classification step; A random forest classifier algorithm is developed to identify heart diseases with higher accuracy.
Step 5: The performance of these work is measured through the following performance metrics, Accuracy, Precision, Recall and F-Score.
Software Requirement: Python – 3.9.6 and Operating System: Windows 10(64-bit)
- Heart disease prediction using distinct artificial intelligence techniques: performance analysis and comparison
Keywords
Logistic regression, Naïve Bayes, Multilayer perceptron
Implementation plan
For perform the Heart Disease Prediction process do the following steps,
- Data collection
- pre-processing
- feature extraction and selection
- Detection based on different ML algorithms
- performance analysis
Step 1: initially Data have been collected from hospitals, diagnostic centers, and clinic centers in Bangladesh.
Step 2: Next, for Minimizing the information perform the Feature extraction and selection by using Correlation-based Feature Subset Selection algorithm.
Step 3: Next, the multilayer perceptron structure, which has three layers—input, hidden, and output—is used to detect heart disease and also implement the process based on logistic regression, Naïve Bayes, K-nearest neighbour (K-NN), support vector machine (SVM), decision tree, random forest, and make a comparison.
Step 4: The performance of these work is measured through the following performance metrics, True Positives, True Negatives, False Positives, False Negatives, accuracy, precision, recall, F1-score, and ROC-AUC score.
Software Requirement: Python – 3.11.3 and Operating System: Windows 10(64-bit)
- Machine learning-based heart attack prediction: A symptomatic heart attack prediction method and exploratory analysis
Keywords
XGBoost, performance measures.
Implementation plan
Step 1: Initially we load the dataset with clinical data.
Step 2: Next we apply the Data analysis; it has been carried out in order to transform data into useful form.
Step 3: Next we perform the analysis of different risk factors and prediction for heart attacks is done using ML approaches of Support Vector Machines, Logistic Regression, Naïve Bayes and XGBoost.
Step 4: Next, based on the analysis process, presenting a machine learning-based heart attack prediction (ML-HAP) method.
Step 5: The performance of these work is measured through the following performance metrics, Accuracy, Precision, Recall and F-Score.
Software Requirement: Python – 3.9.3 and Operating System: Windows 10(64-bit)
5. Heart Disease Prediction using Machine Learning and Deep Learning Algorithms
Keywords
Deep learning, Industries, Artificial neural networks, Prediction algorithms
Implementation plan
Step 1: Initially Medical records and other information about patients are gathered from the UCI repository for prepare the dataset by using the machine learning algorithms.
Step 2: Next we apply the Pre-processing based on various attributes of the patient, like gender, chest pain, serum cholesterol, fasting blood pressure, exang. And we can fill missing and noise values and also balancing the dataset.
Step 3: Next five different machine learning algorithms [Logistic Regression Model, Support Vector Machine (SVM), K-Nearest Neighbours (K-NN), Random Forest and Gradient Boosting] are implemented for classification.
Step 4: Next we perform the Classification step; A random forest classifier algorithm is developed to identify heart diseases with higher accuracy.
Step 5: The performance of these work is measured through the following performance metrics, Percentage of no heart disease and heart disease, Comparison between sex and target feature, Age v/s Cholesterol with the target feature, Kernel density estimate (kde) plot of age v/s cholesterol, Correlation matrix of the attributes and Accuracy comparison of machine learning algorithms.
Software Requirement: Python – 3.9.6 and Operating System: Windows 10(64-bit)
6. A Hybridized Model for the Prediction of Heart Disease using ML Algorithms
Keywords
Measurement, Neural networks, Predictive models
Implementation plan
Step 1: Initially we load the dataset of Cleveland heart disease with ECG images.
Step 2: Next we apply the Genetic Algorithm and PSO algorithm process for extracting important features.
Step 3: Next build the prediction model by using formerly pertaining neural network algorithm.
Step 4: Next, the prediction model will be applied on test data and predict the attacker and to calculate metrics like prediction accuracy.
Step 5: The performance of this work is measured through the following performance metrics, Accuracy, Precision, Recall and F-Score.
Software Requirement: Python – 3.9.3 and Operating System: Windows 10(64-bit)
7. Research of Heart Disease Prediction Based on Machine Learning
Keywords
Support vector machines, Cardiac disease, Data models, coronary heart disease, heart disease prediction
Implementation plan
Step 1: Initially we load the clinical data in the medical field.
Step 2: Next build the cardiac disease prediction model for by using Machine Learning algorithms.
Step 3: Next, implement the machine learning algorithms to achieve classification of patient disease types or prediction of disease risks.
Step 4: The performance of these work is measured through the following performance metrics, Accuracy, Precision, Recall and F-Score.
Software Requirement: Python – 3.11.3 and Operating System: Windows 10(64-bit)
8.Heart Disease Prediction Using Supervised Machine Learning Algorithms
Keywords
Training, Hospitals, health care services
Implementation plan
Step 1: Initially we load the dataset, it contains 1025 patient records including 713 males and 312 females of different ages where 499 (48.68%) patients are normal and 526 (51.32%) patients have heart disease.
Step 2: Next perform the pre-processing, for detect outlier and extreme values based on ReplaceMissingValues filter and the Interquartile Range (IQR),
Step 3: Next, Synthetic minority oversampling technique (SMOTE) was also applied to balance the imbalanced dataset. Thus, some exploratory data analyses (EDA) was performed (such as box plot) to confirm that the dataset is free of outliers.
Step 4: Next, classify the disease by using multilayer perceptron (MP), K-nearest neighbours (KNN), random forest (RF), decision tree (DT), logistic regression (LR) and AdaboostM1 (ABM1) algorithms.
Step 5: The performance of these work is measured through the following performance metrics, Sensitivity, Specificity,
and FPR.
Software Requirement: Python – 3.11.4 and Operating System: Windows 10(64-bit)
9.Prediction of Early Heart Attack Possibility Using Machine Learning
Keywords
Technological innovation, Data mining, feature selection and Diagnosis
Implementation plan
Step 1: Initially we load the clinical dataset.
Step 2: Next, perform the data pre-processing for the noise remove and balance the dataset
Step 3: Next, analysis the risk factors from the pre-processed dataset by using a data-driven prediction model to reach an early diagnosis of heart disease.
Step 4: Next, perform the data / factor classification and detect the heart disease by using Machine Learning algorithm Random Forest classifier.
Step 5: The performance of these work is measured through the following performance metrics, True Positives, True Negatives, False Positives, False Negatives, accuracy, precision, recall, F1-score, and ROC-AUC score.
Software Requirement: Python – 3.11.3 and Operating System: Windows 10(64-bit)
10.Survey of Heart Disease Prediction and Identification using Machine Learning Approaches
Keywords
Classification algorithms, Mathematical model, Clustering algorithms, LSTM and CNN
Implementation plan
Step 1: Initially we load the dataset, it contains text data with the heart rates
Step 2: Next perform the pre-processing based on the Data mining technique.
Step 3: Next, perform the Heart Disease Prediction by using the technique of Data Mining.
Step 4: Next, the encryption complexity can be enhanced using the proposed technique with LSTM and CNN heart disease prediction and prior automatic diagnosis can be achieved.
Step 5: The performance of these work is measured through the following performance metrics, Accuracy, Time, and cost.
Software Requirement: Python – 3.9.3 and Operating System: Windows 10(64-bit)
11.Machine Learning Heart Disease Prediction Using KNN and RTC Algorithm
Keywords
Real-time systems, KNN, Decision Tree Classifier Algorithm
Implementation plan
Step 1: Initially we load the dataset with elemental symptoms and health factors.
Step 2: Next, perform the process of predict the vulnerability by using Machine Learning
Step 3: Next, analysis the risk factors from the pre-processed dataset based on the basic data of the patients like age, sex.
Step 4: Next, perform the data / factor classification and detect the heart disease by using Machine Learning algorithms KNN and decision tree classifier.
Step 5: The performance of this work is measured through the following performance metrics, True Positives, True Negatives, False Positives, False Negatives, accuracy, precision, recall, F1-score, and ROC-AUC score.
Software Requirement: Python – 3.11.3 and Operating System: Windows 10(64-bit)
12. Heart Disease Prediction Using Adaptive Infinite Feature Selection and Deep Neural Networks
Keywords
Sensitivity, Adaptive systems, Feature extraction, Infinite feature selection
Implementation plan
Step 1: Initially we load the ECG dataset.
Step 2: Next, for evaluation purposes, we have combined all the datasets together and then divided the combined dataset into training and test samples with a 20 % percent of the samples allocated for testing.
Step 3: Next, perform the pre-processing, for detect outlier and extreme values based on Replace Missing Values.
Step 4: Next, heart disease prediction using a modified variation of infinite feature selection and multilayer perceptron.
Step 5: The performance of these work is measured through the following performance metrics, accuracy, F1-score, sensitivity, specificity and precision.
Software Requirement: Python – 3.11.4 and Operating System: Windows 10(64-bit)
13.Empirical Analysis of Heart Disease Prediction Using Deep Learning
Keywords
Recurrent neural networks, Health
Implementation plan
Step 1: Initially we load the datasets with a number of patients provided factors.
Step 2: Next build the prediction model for identify the cardiac disease.
Step 3: Next, identify cardiac disease by using the Deep Learning algorithms, like Long-Term Memory Network Model (LSTM), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Densenet, and Bi- LSTM.
Step 4: The performance of these work is measured through the following performance metrics, Accuracy, Precision, Recall and F-Score.
Software Requirement: Python – 3.9.3 and Operating System: Windows 10(64-bit)
14. Heart Disease Prediction using Innovative Decision tree Technique for increasing the Accuracy compared with Convolutional Neural Networks
Keywords
Supervised learning, Innovative Decision Tree Technique, Accuracy rate
Implementation plan
Step 1: Initially we load the five different datasets at each time to record five samples
Step 2: Next perform the pre-processing the medical parameters of cardiac patients to improve the detection rate accuracy.
Step 3: Next, perform the Heart Disease Prediction by using decision tree algorithm.
Step 4: The performance of these work is measured through the following performance metrics, Accuracy, Time, and cost.
Software Requirement: Python – 3.9.3 and Operating System: Windows 10(64-bit)
15.Prediction of Heart Disease Using Machine Learning
Keywords
Conferences, Sensors, symptoms
Implementation plan
Step 1: Initially, collect the data from medical history of 304 different patients of different age groups.
Step 2: Next perform the pre-processing, which is deals with the missing values, cleaning of data and normalization.
Step 3: Next, classifies patient’s risk level by implementing different data processing techniques like Naive Bayes, Decision Tree, Logistic Regression and Random Forest in the Heart Disease Prediction System (EHDPS) model.
Step 4: The performance of these work is measured through the following performance metrics, Accuracy, Time, and cost.
Software Requirement: Python – 3.11.3 and Operating System: Windows 10(64-bit)
16.Heart Disease Prediction Using Different Machine Learning Algorithms
Keywords
Radio frequency, Medical services
Implementation plan
Step 1: Initially we load the dataset, it contains heart disease data from the Cleveland database
Step 2: Next perform the pre-processing based on the Data mining.
Step 3: Next, perform the Heart Disease Prediction model construction by using the technique of Data Mining techniques like reinforcement learning unsupervised, and supervised learning process.
Step 4: Next, perform the heart disease process by using decision tree (DT), random forest (RF), and logistic regression (LR) algorithms.
Step 5: The performance of this work is measured through the following performance metrics, Accuracy, Precision, Recall and F-Score.
Software Requirement: Python – 3.9.3 and Operating System: Windows 10(64-bit)
17. Heart Disease Prediction using Hybrid machine Learning Model
Keywords
Medical diagnostic imaging, Diseases Cleveland Heart Disease Database, Hybrid algorithm
Implementation plan
Step 1: Initially we load the Cleveland heart disease dataset.
Step 2: Next, splitting dataset into test and train data d. Apply Decision tree and Random Forest regression models for training and analysis.
Step 3: Next, Test the trained model and predict values g. Get single input from user and predict heart disease through Hybrid model (Hybrid of random forest and decision tree).
Step 4: The performance of this work is measured through the following performance metrics, True Positives, True Negatives, False Positives, False Negatives, Heart Disease prediction ratio, and ROC-AUC score.
Software Requirement: Python – 3.11.3 and Operating System: Windows 10(64-bit)
18. Heart Disease Prediction using Enhanced Deep Learning
Keywords
Analytical models, Organizations, Logic gates
Implementation plan
Step 1: Initially we load the datasets with a data based on affected person’s heart functionality.
Step 2: Next build the prediction model for identify the cardio disease.
Step 3: Next, identify cardio disease by using the enhanced Deep Learning algorithm, like Enhanced Deep Convolutional Neural Network (EDCNN) with hyper parameters.
Step 4: The performance of these work is measured through the following performance metrics, Accuracy, Precision, Recall and F-Score.
Software Requirement: Python – 3.9.3 and Operating System: Windows 10(64-bit)
19.Heart Disease Prediction using Machine Learning Techniques
Implementation plan
Step 1: Initially we load the datasets with a number of factors like chest pain, cholesterol level, and age of the person.
Step 2: Next calculate the Euclidian distance for select the data point by using K-Nearest Neighbor (K-NN).
Step 3: Next, constructing multiple decision trees of the training data and detect heart disease by using Random Forest algorithm.
Step 4: The performance of this work is measured through the following performance metrics, Accuracy, Precision, Recall and F-Score.
Software Requirement: Python – 3.9.3 and Operating System: Windows 10(64-bit)
20.Hybrid Method for Evaluating Feature Importance for Predicting Chronic Heart Diseases
Keywords
Computational modelling, Forestry
Implementation plan
Step 1: Initially we load the dataset, it contains data of patients with cardiac disease
Step 2: Next perform the pre-processing based on the Data mining.
Step 3: Next, perform the heart disease process by using Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Naïve Bayes, and Random Forest and make a comparison between the algorithm results.
Step 4: The performance of this work is measured through the following performance metrics, Accuracy, Precision, Recall and F-Score.
Software Requirement: Python – 3.9.3 and Operating System: Windows 10(64-bit)
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