Cardiomyopathies are heart muscle diseases that affect the heart’s ability to pump blood efficiently. They often have a genetic origin and can lead to heart failure, arrhythmias, and other life-threatening complications. Early detection is essential for effective treatment planning and improving patient outcomes.
This project aims to develop a machine learning (ML) model capable of detecting and classifying different types of cardiomyopathies at an early stage, by analyzing gene expression data alongside lifestyle and clinical factors.
Gene expression data reflects the amount of amino acids/proteins coded by genes, which can provide insights into genetic markers of cardiomyopathy.
In addition to gene data, we include key lifestyle and clinical indicators that influence heart health:
Lifestyle factors: Dietary habits, smoking, and alcohol consumption, which may contribute to disease progression. Clinical data: Health markers like Creatinine, B-type Natriuretic Peptide (BNP), platelet count, and other blood test results, which are commonly associated with heart function and failure risk. Goal: By integrating genetic data, clinical indicators, and lifestyle factors, the ML model will provide an effective tool for the early detection of cardiomyopathies, allowing for earlier intervention, personalized treatment strategies, and improved long-term outcomes for patients.
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