Heart Disease Prediction Using Machine Learning Project

Under heart disease prediction using machine learning project we aim to make use of various subsets and find the best groupings to increase the dataset’s analytical accurateness. We can predict heart disease by utilizing machine learning that is involved by creating a method that can regulate the possibility of someone can have (or improving) heart disease on the basis of set of medical features. The binary outcome is to predict if the patient is having heart issues or not. Our leading PhD professional team guide will you for MS thesis by framing the correct work plan schedule so don’t hesitate get our team guidance for proper outcome. We run for more than 18+ years and we have also earned online trust for more than 5000+ customers yet we are a reliable team indeed so enroll yourself for more support. Here we guide the setting up of such project:

  1. Problem Definition:

To forecast whether a patient is at risk of emerging heart disease within a specified timeframe on the basis of medical and demographic structures.

  1. Data Collection:

We utilize the widely used dataset for heart disease prediction namely UCI Heart Disease dataset that has the following characters:

  • Age
  • Gender
  • Chest pain type
  • Resting blood pressure
  • Serum cholesterol
  • Fasting blood sugar
  • Electrocardiographic outcomes
  • High heart rate attained
  • Exercise-induced angina
  • ST depression induced by exercise relative to rest
  • Peak exercise ST segment
  • Number of most important vessels colored by fluoroscopy
  • Thalassemia type
  • Analysis of heart disease (target variable)
  1. Data Preprocessing:
  • Handle missing values: To fill missing data points we utilize imputation methods.
  • Categorical Encoding: We change the categorical variables into numerical format by utilizing the techniques like one-hot encoding.
  • Normalization/ Standardization: We normalize the data to maintain the same scale in all numerical attributes.
  • Data splitting: Splitting the data can be of three sets namely training, validation and test.
  1. Exploratory Data Analysis (EDA):
  • To visualize the distribution of different structures and the target variable.
  • Our work discovers the relationship among various characters and the result.
  • We can find the potential outliers or anomalies.
  1. Feature Selection:

            Select the characters that are more applicable for prediction. This can be based on the field knowledge, correlation analysis, or utilizing automated techniques like Recursive Feature Extraction (RFE).

  1. Model Selection:

Our work uses different Machine Learning methods to find the great performer: some of the methods like:

  • Logistic Regression
  • Decision Trees and Random Forest
  • Gradient Boosted Machines (like XGBoost or LightGBM)
  • Support Vector Machines (SVM)
  • Neural networks
  1. Model Training:

We have to train the selected methods by utilizing training data.

  1. Evaluation:

To evaluate the method’s performance, we validate and test the datasets by utilizing the metrics like:

  • Accuracy
  • Precision, recall, F1-score
  1. Optimization:
  • Hyperparameter tuning: The models’ parameters can be adjusted to obtain best performance in our study.
  • Feature Engineering: We have to consider deriving new characters on the basis of previous one or by merging the characters.
  • Ensembling: Utilize the methods like bagging or boosting to merge predictions from multiple models.
  1. Deployment:

            If once you will be fulfilled with the method’s achievements, then execute it in an appropriate setting such as within hospital software or a mobile app for personal health checking.

  1. Feedback and Iteration:

            Sometimes we have to retrain the model with some new data, collect feedback from medical professionals, and make essential modifications.

  1. Ethical Considerations:
  • Bias & Fairness: To make sure that the model does not unintentionally discriminates along any group. We have to check and test for biases.
  • Interpretability: Utilize the tools or methods to make the method’s performance understandable to end-users.
  • Data privacy: Our work makes sure that compliance with data protection regulations, specifically when we handle sensitive health data.

Tools & Libraries:

  • Data Handling & EDA: Some of the data handling & EDA methods like pandas, NumPy, Matplotlib, and Seaborn were used.
  • Machine Learning: We utilize some ML methods like scikit-learn, TensorFlow, Keras and XGBoost.

Final thoughts:

            Heart disease prediction is one of the most important applications of Machine learning in healthcare. These methods can support healthcare professionals in making informed decisions, it is important to visit them as supplementary tools and that cannot be replaced for expert decision. Regular validation, teamwork with medical experts and adherence to ethical standards are the dominant for our success and influence of such project. So don’t delay hurry up contact us for experts touch in your research work. We maintain the work highly confidential which is our key ethics. If you are struggling in paper publishing we would be glad to help you out. Our publishing department work endlessly and publish paper in reputable journals like IEEE, ACM, SCIENCE DIRECT, SCOPUS………

Heart Disease Prediction Using Machine Learning Project ideas

Heart Disease Prediction Using Machine Learning Research Topics

  1. Heart Disease Prediction using Ensemble Learning


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.

  1. Optimized Ensemble model for Heart Disease Prediction using Machine Learning


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.

  1. Using Personal Key Indicators and Machine Learning-based Classifiers for the Prediction of Heart Disease


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.

  1. Heart Disease Prediction Using Machine Learning Algorithm


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.

  1. Machine Learning based Mobile App for Heart Disease Prediction


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.

  1. Application of Machine Learning Algorithms in Predicting the Heart Disease In Patients


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.

  1. Heart Disease Prediction Model using various Supervised Learning Algorithm


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.

  1. Heart Disease Prediction based on Machine learning Technique


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.

  1. Analysis of Heart Disease Prediction using Various Machine Learning Techniques: A Review Study


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.

  1. Early Prediction of Chronic Heart Disease Based on Electronic Triage Dataset by using Machine Learning


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.