Introduction

Heart disease remains a leading cause of mortality worldwide, highlighting the need for advanced diagnostic tools. Integrating artificial intelligence (AI) into medical diagnostics offers a promising approach to enhance the accuracy and efficiency of heart disease detection.

This project aims to develop an AI-based heart disease detection system using a comprehensive dataset of 70,000 individuals. By employing machine learning models like Random Forest, Support Vector Machine, and Neural Networks, the system will accurately distinguish between patients with and without heart disease. Key steps include data preprocessing, visualization, and rigorous model evaluation using metrics such as accuracy, precision, recall, and F1-score.

The trained model will be integrated into a user-friendly Flask web application, featuring an intuitive interface for users to input medical data and receive immediate predictions. The application will utilize Python, HTML, and CSS to ensure accessibility and ease of use.

Additional features include a doctor appointment system, allowing users to browse profiles and schedule appointments, and an integrated chatbot for assistance with frequently asked questions about various health issues.

By combining advanced AI techniques with practical application features, this AI-based heart disease detection system aims to significantly contribute to the early diagnosis and management of heart disease, ultimately improving patient outcomes and saving lives.

Aims and Objectives

Develop a Heart Disease Classification System

Utilize a comprehensive dataset of 70,000 individuals to create a robust system that effectively distinguishes between patients with and without heart disease.

Implement advanced data preprocessing techniques to enhance data quality and ensure accurate analysis.

Data Preprocessing and Visualization

Conduct thorough data cleaning, normalization, and transformation to prepare the dataset for analysis.

Utilize visualization tools to provide comprehensive insights, identify patterns, and facilitate a deeper understanding of the dataset.

Model Training and Evaluation

Train multiple machine learning models on the preprocessed dataset, including Random Forest, Support Vector Machine, and Neural Networks.

Evaluate the performance of these models using metrics such as accuracy, precision, recall, and F1-score to identify the most accurate and reliable model for heart disease prediction.

Fine-tune the models to achieve optimal performance.

Integration into a Flask Web Application

Save the weights of the best-performing model and integrate it into a Flask web application.

Develop an intuitive user interface using Python, HTML, and CSS, allowing users to input their medical data through a form and receive prompt predictions regarding their likelihood of having heart disease.

Ensure the application is user-friendly and accessible.

Additional Features and Deployment

Implement a doctor appointment system within the web application, allowing users to view profiles of different doctors and schedule appointments.

Integrate a chatbot to assist users with frequently asked questions related to various diseases and problems, enhancing user support and engagement.

Deploy the system to ensure seamless access and usability, enabling users to conveniently utilize the heart disease classification functionality.

Problem Statement

Heart disease remains a leading cause of mortality globally, necessitating timely and accurate diagnostic tools. Traditional diagnostic methods are often time-consuming and reliant on subjective interpretation by healthcare professionals. This project aims to develop an AI-based heart disease detection system to provide rapid, reliable predictions and improve early diagnosis and treatment outcomes.

Research Question/Hypothesis

Can an AI-based classification system, trained on a dataset of 70,000 individuals, accurately predict the presence of heart disease?

Dataset details

There are 3 types of input features:

Objective: factual information;

Examination: results of medical examination;

Subjective: information given by the patient.

Features:

Age | Objective Feature | age | int (days)

Height | Objective Feature | height | int (cm) |

Weight | Objective Feature | weight | float (kg) |

Gender | Objective Feature | gender | categorical code |

Systolic blood pressure | Examination Feature | ap_hi | int |

Diastolic blood pressure | Examination Feature | ap_lo | int |

Cholesterol | Examination Feature | cholesterol | 1: normal, 2: above normal, 3: well above normal |

Glucose | Examination Feature | gluc | 1: normal, 2: above normal, 3: well above normal |

Smoking | Subjective Feature | smoke | binary |

Alcohol intake | Subjective Feature | alco | binary |

Physical activity | Subjective Feature | active | binary |

Presence or absence of cardiovascular disease | Target Variable | cardio | binary |

All of the dataset values were collected at the moment of medical examination.

Deliverables

Preprocessed and Enhanced Dataset  Cleaned, normalized, and transformed dataset ready for analysis, ensuring data quality and suitability for machine learning models.

Trained Machine Learning Models  Implementation and evaluation of multiple models such as Random Forest, Support Vector Machine, and Neural Networks, optimized for heart disease classification using performance metrics like accuracy, precision, recall, and F1-score.

Flask Web Application  Development of a user-friendly web interface allowing input of medical data for immediate heart disease prediction using the best-performing model. Includes features for intuitive navigation, data submission, and real-time predictions.

Integrated Doctor Appointment System Addition of a feature enabling users to browse doctor profiles and schedule appointments within the web application, enhancing accessibility to medical care.

Chatbot Integration Incorporation of a chatbot to provide instant support and information on health-related queries, improving user engagement and support within the system.

Future work

Moving forward, our AI-based heart disease classification system aims to enhance model performance through advanced techniques like ensemble learning and deep learning. Integrating wearable devices and electronic health records will enable real-time health monitoring, supporting personalized care strategies. Implementing explainable AI methods, such as SHAP values, will ensure transparency in predictions, fostering trust among healthcare providers and patients.

The system will also expand its scope to predict additional cardiovascular conditions and undergo rigorous validation through clinical trials to confirm its efficacy in real-world settings. These efforts aim to evolve the system into a versatile tool for early disease detection and personalized healthcare, ultimately improving overall patient health outcomes.

By prioritizing advanced techniques, real-time monitoring, and transparency, our AI-based system is set to become an indispensable asset in the fight against heart disease and other cardiovascular conditions.