Implement a system of Heart Disease Detection Using Machine Learning

Project Domain

Machine Learning based project

Introduction

The scope of the project is to implement a machine learning based system that detect and diagnose the heart disease. Cardiovascular diseases are one of the major causes of mortality throughout the world. Early detection is very important for effective and intime treatment can save many lives. Dataset is first step to obtain in order to train a selected model. The system will use patient date, like medical history, diagnostic test and their results, life style factors in order to predict the likelihood of heart disease. Machine learning algorithms can analyse complex patterns in the data that may not be apparent through traditional diagnostic methods. The main objective of the system is to design, implement and evaluate different machine learning model that can predict the heart disease more accurately in patient. The system will help to identify the most relevant features that hep to predict heart disease. It also compares the performance of different machine learning algorithms. This comparison will be based on result accuracy, precision, recall and F1 score. For interact with system, create a user-friendly interface in order to get input of patient data and receive predictions plus results of models.

Steps include collection of data set, mostly features used for heart disease data set are age, gender, blood pressure, cholesterol level, fasting blood sugar etc. Next steps include data pre-processing, splitting of data and algorithm selection. Several machine learning algorithms can be applied to predict heart disease: Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVM), K-Nearest Neighbours (KNN), Neural Networks/Deep Learning, Gradient Boosting Algorithms. The selected algorithm is trained on the training dataset. During training, the algorithm learns the patterns in the data, linking inputs (e.g., cholesterol levels, age) to outputs (heart disease or not). The last step is to evaluate the model on the test set using matrices. You have to create a web based user-friendly interface that allows healthcare professionals to input patient data and receive predictive results.

Functional requirement

Data set collection

1.      The system shall allow user to input patient data including demographic, clinical, and lifestyle information.

2.      The system shall accept various data inputs such as:

•         Age

•         Gender

•         Blood pressure

•         Cholesterol levels

•         Blood sugar levels

•         Smoking habits

•         Chest pain type

•         Maximum heart rate achieved

•         ST depression values

3.      The system shall import the data of patient in batch format like in Excel file.

4.      The system shall validate data to ensure complete record has been upload and only valid data will proceed.

Data Preprocessing

5.      The system shall handle missing data using imputation techniques