Cardiovascular Risk Prediction

International Research Journal of Engineering and Technology (IRJET)

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Volume: 12 Issue: 07 | Jul 2025

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Cardiovascular Risk Prediction Er. Jagmeet Singh1, Er. Charandeep Singh2 1Jagmeet Singh Dept. of Computer science and Engineering, Baba Farid College of Engineering and technology,

Bathinda, Punjab

2Assistant Professor Charandeep Singh Bedi, Dept. of Computer Science and Engineering, Baba Farid College of

Engineering and technology, Bathinda, Punjab ---------------------------------------------------------------------***--------------------------------------------------------------------countless lives. However, diagnosing and treating patients at Abstract - Cardiovascular disease (CVD) impairs the early stages poses a noticeable challenge for cardiologists. function of the heart and blood vessels, which often resulting in death or physical paralysis. Thus, early and Traditional CVD risk-assessment models often assume a automated detection of CVD is important for human linear relationship between each risk factor and CVD lives. Although various studies have goal to achieve this, outcomes. These models tend to simplify complex but there still room for improving performance and relationships, including numerous risk factors with nonreliability. This study makes contribution in this linear interactions. It is essential to incorporate multiple risk direction. It implemented two reliable machine learning factors accurately and examine the nuanced correlations between these factors and outcomes. Till date, no large-scale techniques—multi-layer perceptron (MLP) and Kstudy has utilized routine clinical data and machine learning nearest neighbour (K-NN)—to detect CVD using data (ML) for prognostic CVD assessment. This study aims to from the publicly available University of California Irvine determine if ML can enhance cardiovascular risk prediction repository. Model performance is enhanced by removing accuracy in primary care populations and also identify which outliers and attributes with null values. Experimental ML algorithms yield the most concise and effective results. results show that the MLP model achieves a detection accuracy of 82.47% and an area-under-the-curve (AUC) 1.1 Objectives value of 86.41%, outperforming the K- Nearest 1. Develop a novel cardiovascular risk prediction model Neighbors model. Therefore, the MLP model is using advanced machine learning techniques, integrating recommended for automatic CVD detection. Additionally, comprehensive patient data for improved accuracy. the proposed methodology also applied on detection of other diseases and can be validated using other standard 2. Validate and compare the model's performance with datasets. existing standards, ensuring robustness and generalizability through rigorous testing and evaluation.

Key Words: MLP, AUC, CVD, K-NN.

3. Enhance model interpretability to facilitate clinical integration, providing clear insights into risk factors for personalized patient care.

1. INTRODUCTION Health is most prominent aspect of everyone's life. However, numerous factors, such as work stress, unhealthy lifestyles, and psychological strain, external influences like hazardous work environments, inadequate health services, and pollution, contribute to millions of people worldwide developing chronic ailments like cardiovascular diseases (CVD). These conditions badly affect the heart and blood vessels, which are leading to death or disability. Recent reports shows that a significant proportion of human deaths are due to CVD[1,2]. Conditions associated with CVD include hypertension, hyperlipidaemia, thromboembolism, and coronary heart disease, all are responsible for heart failure, and with hypertension it is known to be as primary cause [3]. In 2012, 7.4 million people died from coronary heart disease, while 6.7 million died from stroke [4]. According to the World Health Organization, nearly 17 million people die annually from CVDs, accounting for approximately 31% of global deaths. Early diagnosis of CVD can potentially save

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1.2 Data Overview The dataset is from an ongoing cardiovascular study on residents of the town of Framingham, Massachusetts. The classification goal is to predict whether the patient has a 10year risk of future coronary heart disease (CHD). the dataset includes data on over 4,000 individuals with coronary heart disease, with 15 characteristics describing each patient.

1.3 Data Cleaning Prior to EDA, cleaning the data is important since it will get rid of anyduplicate information that can have an impact on the results. Education, heartRate, BPMed , totChol BMI , cigsPerDay, and glucose columns have missing or null values.

I have filled these columns for missing values by using KNN

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International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 12 Issue: 07 | Jul 2025

p-ISSN: 2395-0072

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imputer. For columns sex and is_smoking, I have changed the categorical value with a numerical value(like yes to 1 and no to 0, and F to 1 and M to 0).

1.4Exploratory Data Analysis Most people smoke between 0 and 10 cigarettes a day. The risk of coronary heart disease rises with growing age to age 63 and then declines beyond that Men have a higher propensity for developing coronary heart disease (CHD) than women. Women, in contrast, are at a higher risk of stroke, which often occurs at an older age. Compared to nonsmokers, smokers have most likely chance of developing coronary heart disease.

Figure no. 2: Impact of systolic blood pressure on the target variable

1.5Feature Engineering Impact of heart rate on the target variable: People with high heart rates have a high risk of having CHD in the next 10 years. Impact of cholesterol level on the target variable: People with high cholesterol levels have a high risk of having CHD in the next 10 years. So impact of age on the target variable: Older people have a high risk of having CHD in the coming 10 years. Impact of Body Mass Index on the target variable: People with high body mass index have a high risk of having CHD in the next 10 years. Impact of systolic blood pressure on the target variable: People with high systolic blood pressure have a high risk of having CHD in the next 10 years. Impact of Glucose on the target variable: People with high Glucose have a high risk of having CHD in the next 10 years. Insights Using Feature Engineering.

Figure No. 3: Imapct of systolic blood pressure on the target variable 1.5 Data preparation The high correlation between: 1. Cigs Per Day and is smoking 2. SysBP and Prevalent Hype 3. DiaBP and SysBP Combined SysBp and DiaBP to denote a new feature pulse rate.

Dropping Cigs per Day, is smoking, SysBP, Prevalent Hype, and DiaBP columns from the dataset.

Figure no. 1: Impact of heart rate on the target variable

Added the columns like, BMI bucket, pulse pressure and age bucket. The dependent column will be predicted as that is the target variable named “Ten Year CHD. Steps involved: 1.

After loading the dataset, performed this method by comparing our target variable that is 10-year risk of future coronary heart disease (CHD) with other independent

Figure no.3: Impact of cholesterol level on the target variable

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Exploratory Data Analysis :

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e-ISSN: 2395-0056

Volume: 12 Issue: 07 | Jul 2025

p-ISSN: 2395-0072

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variables. This process helped us figuring out various segments and relationships among the target and the independent variables. It gave us a better idea of which feature behaves in which manner compared to the target variable.

2.

Null values Treatment:

3.

This dataset contains a large number of null values which might tend to disturb our accuracy hence used a KNN-imputer to perform missing value imputation, and processed data to remove outliers.

Figure No.5: Representation of data of BP, cholesterol and BMI Figure No. 4: Data of people smoke cigarettes per day between 0-10.

Feature Selection In these steps we used algorithms like ExtraTree classifier to check the results of each feature that is, which feature is more important compared to our model and which is of less important.

Next, there is Chi2 used for categorical features and ANOVA for numerical features to select the best feature which we will be using further in our model. Fitting different models For modelling we tried various classification algorithms like: 1. Logistic Regression 2. SVM Classifier 3. Random Forest Classifier 4. Grid Search CV

Figure No.5: Histogram plot for the age of people in this dataset

Encoding of categorical columns We used One Hot Encoding to produce binary integers of 0 and 1 to encode our categorical features because categorical features that are in string format cannot be understood by the machine and needs to be converted to numerical format.

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Volume: 12 Issue: 07 | Jul 2025

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[6] Jaworski M, Duda P, Rutkowski L. New splitting criteria for decision trees in stationary data streams. IEEE Trans Neural Netw Learn Syst. 2018;29:2516–29.

SMOTE boosting for features To solve the issue of class imbalance, SMOTE boosting was used to over-sample the minority class observations.

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2. CONCLUSIONS The patient's health may suffer greatly as a result of this Data were resampled because they weren't balanced. High accuracy can be achieved with imbalanced data, however, in these situations, recall, precision, and F1 score must be considered. Wih the KNN-imputer, it is performed for missing value imputation, and processed data to remove outliers. To solve the issue of class imbalance, SMOTE boosting was used to over-sample the minority class observations. Newer elements like age bucket, BMI bucket and pulse pressure that helped to explain the separation in the Risk were created using the information from EDA. Due to the parametric relationship in the data, a logistic regression model was implemented, and it was successful in achieving a Recall of 74.5%. Even though the recall score for SVM was 81.9 %, SVM is not an interpretable model, thus I chose an interpretable model for this situation. All measures, including Precision, Recall, Accuracy, and F1 score were evaluated for each model. Based on this analysis, Logistic regression can identify positive cases with a 74.5% Recall.

Using a decision tree, positive cases may be predicted with a recall of 49%. With the help of Random Forest, positive cases may be predicted with a 49% Recall. Using a Support Vector Machine, positive cases can be predicted with an 81.9% Recall. Using Grid Search CV, positive cases can be predicted with 81.9% Recall

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[1] Cardiovascular diseases (CVDs). http://www.who.int/newsroom/factsheets/detail/cardiova scular-diseases-(cvds accessed on 30/9/2018.

[15] https://archive.ics.uci.edu/ml/datasets/Heart+Disease.

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BIOGRAPHY

[3] Baharvand-Ahmadi B, Bahmani M, Zargaran A. A brief report of Rhazes manuscripts in the field of cardiology and cardiovascular diseases. Int J Cardiol. 2016;207:190–1.

[4] Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N. Can machine-learning improve cardiovascular risk prediction using routine clinical data?

PLOS ONE. 2017;12(4):e0174944.

Jagmeet Singh, Dept. of Computer Science and Engineering, Baba Farid College of engineering and technology, Bathinda, Punjab.

1’st Author Photo

[5] Yan H, Ye Q, Zhang T, Yu D-J, Yuan X, Xu Y, et al. Least squares twin bounded support vector machines based on L1norm distance metric for classification. Pattern Recogn.

2018;74:434–47.

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