HDPM: An Effective Heart Disease Prediction Model for a Clinical Decision Support System

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1. MALLA REDDY COLLEGE OF ENGINEERING (Approved by AICTE, Permanently Affiliated to JNTUH) Recognized under Section 2(f) & 12(B) of the UGC Act 1956, An ISO 9001:2015 Certified Institution. Maisammaguda, Dhulapally, post via Kompally, Secunderabad - 500100 DEPARTMENT OF CSE-DS MINI PROJECT REVIEW ACADEMIC YEAR: 2023-2024 YEAR: III SEM:II DATE :12-07-2024

2. HDPM: An Effective Heart Disease Prediction Model for a Clinical Decision Support System TEAM MEMBERS 1.JUKANTI SUCHITRA-21Q91A6787 2.NALLANAGULA ADHARSH-21Q91A67A5 3.SYED NAYAB RASOOL-21Q91A67B3 4.G.RAJESH-22Q95A6710 Under the Guidance of Mr . GM BRITTO HEAD OF THE DEPARTMENT -(CSE-DS)

3. CONTENTS ABSTRACT INTRODUCTION LITERATURE SURVEY EXISTING SYSTEM PROPOSED SYSTEM HARDWARE/SOFTWARE REQUIREMENTS ARCHITECTURE MODULES METHODOLOGY ALOGRITHM

4. abstract Heart disease, one of the major causes of mortality worldwide, can be mitigated by early heart disease diagnosis. A clinical decision support system (CDSS) can be used to diagnose the subjects' heart disease status earlier. This study proposes an effective heart disease prediction model (HDPM) for a CDSS which consists of density-based spatial clustering of applications with noise (DBSCAN) to detect and eliminate the outliers. Two publicly available datasets (stat log and Cleveland) were used to build the model Therefore , early treatment could be conducted to prevent the deaths caused by late heart disease diagnosis .

5. INTRODUCTION Heart disease is a cardiovascular disease (CVD) that remains the number one cause of death globally and contributes to approximately 30% of all global deaths. If unmitigated, the total number of deaths globally is projected to increase to around 22 million in 2030. The American heart association reported that nearly half of American adults are affected by cvds, equating to nearly 121.5 million adults. Several risk factors that can lead to heart disease include unhealthy diet, physical inactivity, and excessive use of tobacco . These risk factors can be minimized by practicing good daily lifestyle such as salt reduction in the diet, consuming fruits and vegetables, doing regular physical activity, and discontinuing use of tobacco and alcohol which eventually could help to reduce the risk of heart disease.

6. LITERATURE REVIEW Several studies have reported the development of heart dis-ease diagnosis based on machine learning models with the aim of providing an HDPM with enhanced performance. Two publicly available heart disease datasets, namely stat-lohig and Cleveland, have been widely used to compare the performance of prediction models among researchers. The results revealed that the proposed model achieved the highest performance among all the models with accuracy , sensitivity and specificity of 88.3%,84.9% and 93.3% .

7. Existing systems Two publicly available heart disease datasets, namely Statlog and Cleveland. Statlog dataset, a heart disease clinical decision support system based on chaos firefly algorithm and rough sets-based attribute reduction (CFARS-AR) was developed by Long et al . Verma et al. (2016) developed a hybrid prediction model based on correlation feature subset (CFS), particle swam optimization (PSO), K-means clustering and MLP . Disadvantages In the existing work, the system is poor performance due to lack of xgboost machine learning. This system is less performance due to lack of heart disease classification techniques.

8. PROPOSED SYSTEM We proposed HDPM by integrating DBSCAN outlier detection, SMOTE-ENN, and xgboost to improve prediction accuracy . Therefore, we propose an effective HDPM for a CDSS which consists of dbscan-based to detect and eliminate the outliers. Improving accuracy of heart disease prediction model We proposed HDPM by integrating DBSCAN outlier detection, SMOTE-ENN, and xgboost to improve prediction accuracy. Real case system development We designed and developed the prototype of the system to show the feasibility and applicability of our proposed model for real-world case study. advantages The system is fast and reliable due to presence of support vector machine (SVM).

9. HARDWARE REQIUREMENTS Processor - Pentium-IV Ram -8 GB ( MIN) Hard Disk - 512 GB Key Board - Standard Windows Keyboard Mouse - Two or Three Button Mouse Monitor -LENOVO

10. Software requirements Operating system -Windows 10 Ultimate Coding Language - Python Front-End - Python Back-End - Django-ORM Designing - HTML,CSS,JS Data Base - MySql (WAMP Server)

11. architecture

12. MODULES Service provider In this module, the service provider has to login by using valid user name and password. After login successful he can do some operations such as view all heart disease data set details, search heart disease data set details, diagnose and identify heart disease,view all remote users, diagnose and identify normal user, diagnose and identify abnormal user,view cholesterol results, view heart beat results,. View and authorize users In this module, the admin can view the list of users who all registered. In this, the admin can view the user’s details such as, user name, email, address and admin authorizes the users. Remote user In this module, there are n numbers of users are present. User should register before doing any operations. Once user registers, their details will be stored to the database. After registration successful, he has to login by using authorized user name and password. Once login is successful user will do some operations like ADD HEART DISEASE DATA SETS, SEARCH ON HEART DISEASE DETAILS, and VIEW YOUR PROFILE.

13. METHODLOGY

14. Algorithm DBSCAN DBSCAN stands for density-based spatial clustering of applications with noise. It is a popular unsupervised learning method used for model construction and machine learning algorithms. It is a clustering method utilized for separating high-density clusters from low-density clusters.

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