Heart Disease Prediction with Machine Learning & Analysis | Exploratory Data Analysis

The Need To Move From Diagnosis To Heart Disease Prediction

Introduction

Presently most people approach treatment of heart disease through the diagnosis route. For instance, after the manifestation of symptoms, individuals approach healthcare for tests and diagnosis. Based on the results, treatment commences. However, it is best to adopt a better approach, through heart disease prediction. This will give a better outcome, as the prediction is achieved much before experiencing symptoms that push an individual to undergo diagnosis.

Overview Of Heart Disease Prediction Using ML

This approach offers accurate and personalized predictions and involves the following steps. Here is a quick look at the steps to understand heart disease prediction better.

Data – ML models rely on data from health records, imaging reports, genetic information, results of lab tests and other wearable devices. This data includes input such as age, blood pressure, cholesterol levels, sugar levels, lifestyle, family medical history etc.
ML Models – There are different ML models including supervised learning, and common algorithms.
Data Preprocessing – Here, data integrity is achieved by imputing missing data or excluding the missing data. Inputs are converted to numerical data for the purpose of processing.
Model Training – Following this, the model is trained on patterns using the dataset.
Evaluation – Metrics are laid down to measure the accuracy of the prediction.
Interpretability – The next stage involves use of ML models to interpret features that contribute the most towards predictions. This helps in understanding the different factors that influence the risk of heart disease.

New Methods In Machine Learning Heart Disease Prediction

Latest methods in ML have made it possible to utilize sophisticated models, and improve interpretability of heart disease prediction. These methods include:

Image analysis – Medical images, such as echocardiograms, CT scans, and MRIs are analysed to detect patterns in heart structure, function, and blood vessel plaques. This offers early indicators of disease.
Interpretation – Advances have helped to improve interpretability, highlighting the contribution of features to specific predictions.
Relationship between Patients – New methods help identify relationships between patients on the basis of various inputs. For instance, clinical features, family history, and lifestyle factors, compiling a dataset of patients with similarities.
Rare and special cases – ML models are trained to handle limited data on rare heart conditions.
Comorbidity – As heart conditions are known to coexist with diseases like diabetes or hypertension, prediction looks at multiple diseases or risk factors.

What Is Meant By Exploratory Data Analysis On Heart Disease Prediction?

This is an important step in heart disease prediction utilizing data analysis. This relies on statistical techniques and visualization methods to make sense of patterns and relationships within datasets. This helps in applying complex analytical methods. Let us take a look at this in the context of heart disease prediction.

Dataset – This begins with collecting data, such as patient demographics, medical history, lab test results, imaging data, lifestyle and past diagnoses.
Data Cleaning – The data is then prepared by addressing missing data points. This may require removal of records, imputing certain values and identifying certain unique values that may change the results.
Visualization – This involves the use of histograms for displaying numerical variables, box plots, scatter plots, and heatmaps to show correlation and identify relationships that influence risk factors.
Patterns – Analysing trends over time, like changes in risk factors or outcomes among various age groups.
Risk factors – Identification of key risk factors linked to heart disease helps devise appropriate strategies and guidelines.

Example Of Use Case Diagram For Heart Disease Prediction Using Machine Learning

Use case diagram helps to visually explain the manner in which healthcare providers, patients, and data scientists, interact in the approach. Here is a description to illustrate heart disease prediction. Stakeholders include healthcare providers, patients, data scientists, and system administrators.

The process

Patient data is furnished by healthcare providers and patients.
The machine learning model is trained by selection of data, preprocessing, and use of historical patient data.
Prediction relies on analysis of the input data, by generating a score that will indicate the possibility of heart disease.
Patient records are updated on the basis of the prediction and recommendations.
The progress of the patient is monitored.

How To Predict Genetic Heart Disease?

Genetic components of heart disease can be predicted using genetic testing. This involves assessing the genetic as well as non-genetic factors to identify people at risk. Here is a quick overview of genetic heart disease prediction

Reasons for heart disease due to genetics – This is mainly from genetic mutations that impact the structure of the heart and its function. For instance, familial hypercholesterolemia is a condition that increases cholesterol levels. Similarly, hypertrophic cardiomyopathy causes thickening of the heart muscle. Long QT Syndrome is another genetic condition that causes sudden cardiac arrest.
Family history – People with a family history of heart disease certainly require genetic counseling. This collects inputs to identify patterns of hereditary heart disease.
Genetic testing – Advanced genetic testing facilities in Tamil Nadu offer individuals the opportunity to get tested for future risk of heart disease. Saliva or blood samples are collected and analysed to identify risks. The reports help in recommending suitable treatment for the desired outcomes. Tests may include testing specific genes linked with hereditary heart conditions, for instance, MYH7 for hypertrophic cardiomyopathy. Other tests include adopting a broader approach to look at multiple genes and identify mutations associated with genetic heart diseases. The third category is utilisation of advanced techniques to sequence the genome, and identify rare mutations.

In addition to the above, data is also collected about lifestyle of the patient. This includes diet, exercise, smoking or drinking habits and other exposure to environmental hazards. Existing medical conditions that interact or influence genetic predispositions are also evaluated to predict heart disease risk.

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Guru Shetty

Shetty B s a cardiologist in Chennai, with extensive experience in the field. He completed his MBBS from Madurai Kamaraj University, followed by an MD in General Medicine and a DM in Cardiology from The Tamil Nadu Dr. M.G.R. Medical University (TNMGRMU).