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Research Proposal on Machine Learning in Heart Disease Diagnosis

Research Proposal on Machine Learning in Heart Disease Diagnosis

  Over the past several decades, heart-related disorders have been the key basis of death worldwide. Heart disease is one of the most prevalent and challenging medical conditions. Heart disease has links with various risk factors. Thus it requires more precise, reliable, and practical approaches to make an early diagnosis to accomplish apt management of heart disease.
  A lot of research is carried out in heart disease prediction and recognition via machine learning techniques to analyze large complex medical data, assisting medical experts in predicting heart disease. Current improvement of machine learning applications describes the usage of electrocardiogram (ECG) and patients data for feasibly identifying heart disease earlier. Dependable and instant heart disease diagnosis provided the help of machine learning classification.

Significant Types of Heart Disease: Coronary artery disease, Heart failure, Congenital heart disease, Cardiomyopathy, Abnormal heart rhythms, Congestive heart failure.

Popular Machine Learning Techniques in Heart Disease Diagnosis: Machine learning is the efficient learning paradigm used for several cardiovascular disorders to impart better diagnosis support in the healthcare system. Machine learning algorithms utilize suitable datasets to predict hidden patterns of heart disease. Some machine learning techniques are described below to empower cardiologists in cardiovascular practice and research.
Supervised Learning: The supervised learning models are widely applied for heart disease prediction and utilize labeled data to find hidden patterns. Linear and logistical regression, Support Vector Machines (SVM), neural networks, random forest, gradient boosted trees, decision trees, and naive Bayes are the supervised machine learning algorithms.
Unsupervised Learning: This learning model employs a clustering concept with unlabeled data to predict the hidden patterns. K-means clustering and Principal Component Analysis (PCA) are unsupervised learning algorithms.
Reinforcement Learning: Reinforcement learning is the budding concept in heart disease detection. It learns from the experience based on its correlation with the environment to determine the defects.

Key Applications of Machine learning in Heart Disease: Heat disease is one of the most ultra-hazardous disorders due to its complexity in diagnosis. Thus, real-world monitoring and prevention systems apply the machine learning paradigm.
Diagnosing heart failure: Present methods for diagnosing heart failure rely on a patient’s history, physical exam, and image-based methods aim to enhance diagnosis with the help of electrocardiography, echocardiography, Electronic Health Record (EHR) data, and other sources.
Precision medicine in heart failure: Research in heart failure focusing on enhancing precision includes stratifying patients with heart failure and identifying its advanced treatments and therapies.
Incidence and outcome prediction: In recent days predicting the incidence and outcome of patients with heart failure is complicated and reports many risks; thereby, machine learning has been applied to improving the prediction of heart failure onset and outcomes by predicting the survival of the patients.

Future Directions of Machine Learning in Heart Disease Diagnosis: The machine learning paradigm discovers sophisticated patterns in medical data and possesses the potential to develop cardiovascular care by supporting heart disease detection and prediction. Few of the future findings are highlighted here for further development.
• Machine learning assisted heart disease diagnosis with imbalanced data still uninvestigated aspects and much potential to unclose in the forthcoming years.
• Owing to the small size of the available dataset, focusing on the generation large amount of data to be enabled in machine learning-based automated heart disease detection.
• Instigating multi-modal approaches in heart disease detection will provide a better and more efficient outcome to support heart-related problems.
• Applying deep neural networks for heart disease diagnosis will impart more generalizable detection and prediction outcomes.