Machine learning based heart disease prediction concept comprises developing a predictive framework that decides the severity of heart disease patients by considering several characteristics or forecasters. Contact phdtopic.com explore more in the area of Heart Disease Prediction Using Machine Learning. Here, we discuss about the construction of our project:
- Describe the Scope:
It is essential for us to find out what kind of disease or condition we are concentrating on (for instance: heart failure, coronary artery disease) and also important to define what we are intending to forecast (for example: presence of heart illness, the severity stage or the progression).
- Gathering of Data:
For forecasting heart disease, collection of dataset with important characteristics is essential. Initially, we utilize publicly available datasets such as the UCI Machine Learning Repository’s Heart Disease dataset. The dataset must contain the following features such as:
- Demographic information (age, sex)
- Lifestyle attributes (alcohol consumption, smoking)
- Cholesterol levels
- Blood pressure levels
- Blood sugar levels
- Exercise-based metrics
- Electrocardiographic outcomes
- History of illness
- Existence or absence of chest pain
- Data Preprocessing:
By managing missing values, eliminating repeated features and outliers, we clean the data. Our work focuses on data standardization or normalization process and encodes categorical attributes as required.
- Exploratory Data Analysis (EDA):
We investigate the data and visible patterns and correlations among characteristics and the results (presence of heart disease) through the utilization of statistical methods and visualization tools.
- Feature Selection:
By utilizing statistical methods, correlation analysis, or feature importance scores based on machine learning framework, we find out the most relevant features for heart disease forecasting.
- Model Chosen:
Our project carries out the experiment using various machine learning methods. Some frequently employed methods for heart disease forecasting are mentioned below:
- Decision Trees
- Support Vector Machines (SVM)
- Neural Networks
- Logistic Regression
- Random Forest
- K-Nearest Neighbors (KNN)
- Model Training & Validation:
Our work divides the dataset into two sets like training and validation. We train our framework using training data and validate its efficiency by utilizing validation data. In terms of various proper metrics like accuracy, precision, recall, F1-score and ROC-AUC, we carry out the binary categorization problem.
- Hyperparameter Tuning:
To enhance the framework’s performance, we optimize it by adjusting hyperparameters through the use of techniques such as Random search or Grid search.
- Evaluation of Model:
To examine the performance of our framework with new unseen data, we evaluate our final framework’s efficiency using separate test data.
- Deployment:
We create a service or application that utilizes the framework to offer forecasting. Construction of a web interface where users can feed characteristics data and attain forecastings also included in this process.
- Monitoring & Maintenance:
After the successful implementation, frequently track our framework’s efficiency to check its accuracy periodically. By utilizing new data, we reconstruct our framework over time.
Utilization of Tools & Libraries:
- Data Analysis & Model Construction: For these purposes, we use Python libraries like NumPy, matplotlib, pandas, seaborn and Scikit-learn.
- Advanced Machine Learning: In neural networks, our project considers PyTorch, TensorFlow or Keras.
- Web Application: We use HTML, JavaScript or CSS for frontend and Django, Flask for backend.
- Deployment: GCP, AWS, Heroku or Azure assists us in the deployment phase.
- Version Control: To track modifications and collaboration, Git is useful for us.
Moral Considerations:
- Check whether we keep the data confidential and secure.
- Be clear about our framework’s accuracy and challenges.
- To prevent unfairness, we ensure whether the dataset is different and representative.
Ideas:
- To validate results, our work suggests associating with healthcare experts.
- For expert’s feedback, distribute our framework’s performance metrics and techniques.
- We interpret the concept that machine learning frameworks help us in the decision making process but must not be the whole contributing aspects in diagnosis.
Machine learning plays a vital role in early identification and forecasting approaches. In the healthcare domain, heart disease prediction is a crucial area. Therefore, it is very important for us to consider robust testing, validation, compliance with clinical experts and moral regulations when we carry out the actual-world deployment.
Heart Disease Prediction Using Machine Learning Thesis Ideas
Check out the latest projects completed by our team! We’re here to assist you in selecting the perfect research topic that aligns with your interests. We’ll present you with a variety of options to choose from for your research. Our team is excited to lend a hand with your research thesis.
- Heart Disease Prediction using Ensemble Learning
Keywords:
Disease prediction, Isolation Forest, Ensemble learning
Our paper utilizes stack ensemble learning method to predict cardiac illness by utilizing variation of heterogeneous weak learners. We include the methods like MLP classifier, DT classifier, SVC and LR. We utilize these ML methods were covered stack-based Ensemble classifier by using this we can find the presence and absence of disease symptoms. We used meta-classifier to combine classification methods. We used LR metaclassifier-based technique to estimate the performance of ML. We utilize SMOTE to handle unbalanced data.
- Optimized Ensemble model for Heart Disease Prediction using Machine Learning
Keywords:
Heart Disease prediction, Ensemble, Healthcare, preprocessing
The goal of our paper is to predict the risk of heart attacks or disease. We have to gather the dataset from open source platform Kaggle ML Repository. We proposed an optimized ensemble method by utilizing different modern ML methods like bagging, RF, Kstar and RF. Our optimized ensemble method provides acceptable predictive risk of heart disease and to enhance patient result.
- Using Personal Key Indicators and Machine Learning-based Classifiers for the Prediction of Heart Disease
Keywords:
Machine learning, coronary heart disease, myocardial infarction, Stochastic gradient descent, Decision tree classifier, Random Forest classifier
We discover various ML methods and data separations to measure each method’s accuracy, precision and recall. We utilize personal key indicators to predict heart disease. We gave greater significance to combine ML into heart disease prediction and can aware people at dangers by utilizing personal key indicators. We also suggested multiple models in this paper and produce high accuracy when utilizing the RF classification and data split.
- Heart Disease Prediction Using Machine Learning Algorithm
Keywords:
Effective Heart Disease Prediction System, Heart Disease, Multilayer Neural Network
Our study uses affinity propagation clustering and NN to construct an adequate heart disease prediction system for forecasting the risk level of heart disease. The methods can predict by utilizing medical criteria such as age, gender, etc. We can predict the patient’s risk of emerging heart disease by EHDPS. A multilayer NN contains back propagation that can working as training approach. We utilize affinity propagation to perform clustering. We also utilized ANN. Our proposed diagnostic approach predicts the risk level of heart disease.
- Machine Learning based Mobile App for Heart Disease Prediction
Keywords:
PHR, MIT App Inventor, Firebase database, Logistic Regression, ANN Multilayer Perceptron, Random Forest
We utilize different measures to predict cardiac disease. The aim of our study is to make a mobile app that can decrease the price of medical tests while it also avoids human bias. The result of our research is to predict cardiac disease. We use different ML methods like LR, ANN MLP and RF. Our RF method gives the best performance while compare to other two methods in term of accuracy. Our study works with RF to predict heart health and construct mobile app with MIT App inventor and store data in firebase database.
- Application Of Machine Learning Algorithms In Predicting The Heart Disease In Patients
Keywords:
Data mining, Naïve Bayes, Decision Trees, prediction
Our paper applies data mining methods to predict heart disease. Our dataset contains attributes like age, gender, blood pressure, etc. We can analyse the parameter to predict the possibility of patient prone to heart disease in future. We applied some ML methods for prediction and classification such as NB, DT and NB with K-means clustering. We have employed these methods to train the dataset and to generate a binary classification. Our proposed system gives the better prediction of heart disease.
- Heart Disease Prediction Model using various Supervised Learning Algorithm
Keywords:
SVM, KNN, AUC, Supervised Learning
Our paper uses supervised machine learning methods for predicting cardiac disorders have been analysed and linked by utilizing medical records UCI ML repository. We observe the effectiveness of different methods like KNN, LR models and SVM. The AUC can be utilized to estimate the effectiveness of different methods by utilizing the AUC score. AUC with LR gives the highest accuracy.
- Heart Disease Prediction based on Machine learning Technique
Keywords:
Heart disease detection, cardiovascular disease
We offer a number of various ML methods to predict cardiovascular disease based on an investigation of clinical data of patients. We use totally four separate classification method for prediction that includes MLP, SVM, RF and NB methods. We have to clean the data and then the features decided upon before the methods were built. Our LR classifier gives the best performance.
- Analysis of Heart Disease Prediction using Various Machine Learning Techniques: A Review Study
Keywords:
Coronary illness
Our paper concentrates on coronary illness forecast, using AI methods, Coronary illness is projected frequently. The intension like RF, KNN and choice tree were used. We can predict the complete analysis of heart disease and we can investigate the prototype by utilizing each computation. We can use the ML by researchers must speed up the creation of software that can aid physicians with prognosis and diagnosis of heart ailment. The main goal of our work is to predict patient’s heart state by utilizing ML methods.
- Early Prediction of Chronic Heart Disease Based on Electronic Triage Dataset by using Machine Learning
Keywords:
Chronic Heart Disease, Healthcare services, Medical Informatics
Traditional method for prediction and analysis can be limited due to the complexity of the data and correlations. The first method we utilized to improve the method by utilizing ML methods and ML can observe patient information and imaging scans to find the pattern and predict the possibility of predicting heart disease or cardiac event. Supervised Learning techniques we used are SVM, ANN, AdaBoost, LR, KNN were utilized to find correlation in CHD data improves prediction rate. Our SVM gives the best accuracy.