[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120424-en":3,"doc-seo-120424-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120424,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","A Comparative Study between Neural Network Models and Standard Machine Learning Models in Heart Disease Prediction","This applied research project evaluates how neural network models compare with standard machine learning approaches for predicting heart disease. The study is framed within a Master of Science in Data Analytics program and uses structured research planning, including clearly described data sources, model design, and end-to-end procedure. It covers key neural network architectures, data pre-processing, feature selection, modelling, ethical considerations, and both descriptive and inferential analyses to support evidence-based conclusions on predictive performance.","A Comparative study between neural network models and standard machine learning models in heart disease prediction  \nDhanush Vinay Uttarkar  \n10611336  \nApplied Research Project submitted in partial fulfilment of the  \nrequirements for the degree of  \nMasters of Science in Data Analytics  \nat Dublin Business School  \nSupervisor: Dr. Vivek Kshirsagar  \nAugust 2023  \nDECLARATION  \nI, Dhanush Vinay Uttarkar, declare that the dissertation that I have submitted to Dublin Business School for the award of MSc in Data Analytics is the result of my own investigations, except where otherwise stated, where it is clearly acknowledged by references. Furthermore, this work has not been submitted in whole or in part for any other degree or qualification.  \nSigned: Dhanush Vinay Uttarkar  \nStudent Number: 10611336  \nDate: 29/08/2023  \nACKNOWLEDGMENTS  \nFirst, I would like to thank my parents and my brother for their continuous support and help without who I would never be here. Their continuous support has been critical in my time away from home achieving my educational goals.  \nNext, I would like to thank my guide Dr Vivek Kshirsagar who helped me and guided me through the entirety of this project, helped me solve any problems I encountered and rid any doubt I had with regard to this applied research project. I would also like to extend my thanks to the teaching staff who imparted their knowledge to us through the year teaching us new and important things which is fundamental to my growth without who this project would be very difficult to complete.  \nLast but not least, I would like to thank Dublin Business School for giving me this amazing opportunity to not only showcase my skill but also implement what we learnt throughout the year in the course and special thanks to my friends and classmates who helped me and collaborated with me to enhance the learning experience.  \nTable of Contents  \nDECLARATION ................................................................................................................................... 1  \nACKNOWLEDGMENTS .................................................................................................................... 2  \nABSTRACT ........................................................................................................................................... 4  \nPROBLEM STATEMENT ................................................................................................................... 5  \nKEY LITERATURE ............................................................................................................................. 6  \n3.1 Introduction ........................................................................................................................... 6  \n3.2 Literature Review ................................................................................................................. 6  \n3.3 Our Contribution .................................................................................................................. 9  \nRESEARCH APPROACH ................................................................................................................. 10  \n4.1 Data source .......................................................................................................................... 10  \n4.2 Design ................................................................................................................................... 15  \n4.2.1 Artificial Neural Networks ......................................................................................... 15  \n4.2.2 Convolutional Neural Networks ................................................................................ 20  \n4.2.3 Recurrent Neural Networks ....................................................................................... 25  \n4.2.4 Training an Artificial Neural Network...................................................................... 32  \n4.2.5 Approximation f","cbCailpnKMnF2Y0k","https://ap.wps.com/l/cbCailpnKMnF2Y0k","pdf",1383570,1,51,"English","en",105,"# Declaration\n# Acknowledgments\n# Abstract\n# Problem Statement\n# Key Literature\n## Introduction\n## Literature Review\n## Our Contribution\n# Research Approach\n## Data source\n## Design\n## Procedure\n## Ethics\n## Data Analysis\n# Result\n## Introduction\n## Descriptive statistics\n## Inferential statistics\n# Conclusion\n# References","[{\"question\":\"What is the main goal of the study on heart disease prediction?\",\"answer\":\"The study aims to compare neural network models with standard machine learning models for heart disease prediction and evaluate their relative performance.\"},{\"question\":\"Which neural network architectures are included in the research approach?\",\"answer\":\"The research design includes Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks, along with training and related approximation functions.\"},{\"question\":\"How does the project handle data preparation and modelling?\",\"answer\":\"The approach includes data pre-processing, feature selection, modelling steps, and a full procedure leading into data analysis and results.\"}]","A Comparative Study between Neural Network Models and Standard Machine Learning Models in Heart Disease Prediction | 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