Case studies

Advance Cancer Detection Leverages DNA-Based Machine Learning for Enhanced Accuracy

About the client

Our client, a pioneering medical device innovator based in the United States, specializes in revolutionary non-invasive technologies aimed at early cancer detection. They have developed a unique, office-based targeted cell collection device coupled with a sophisticated lab-based DNA biomarker test.

This test is specifically designed to detect precursor conditions for types of cancer, focusing on enhancing the accuracy of cancer risk assessments. By leveraging the potential of DNA methylation levels in key genes, such as VIM and CCNA1, the company is at the forefront of personalized medicine, offering new hope for early and accurate cancer detection.


The project embarked upon by with this medical innovator revolved around enhancing the sensitivity of the client's DNA-based data analysis. This enhancement focused particularly on the VIM and CCNA1 genes, known for their critical role in signaling cancer development based on methylation levels. A high percentage of methylation in these genes indicates a higher likelihood of cancer.

Business Challenge

The primary challenge faced by our client was to significantly enhance the sensitivity of their DNA-based data analysis process. This endeavor focused on the accurate detection of precursor conditions for specific cancers, utilizing the methylation levels of the VIM and CCNA1 genes as biomarkers.  

Our challenge was to incorporate demographic data such as age, gender, and race to refine benchmark scores, with the goal of achieving over 90% sensitivity and maintaining a high specificity level   for reliable cancer risk assessments.

Our Approach and Solution

  • Identified the challenge as a binary classification problem, focusing on the sensitivity and specificity of detecting cancer precursors through DNA methylation analysis.
  • Multiple machine learning algorithms were trained, including Logistic Regression, Random Forest, Decision Tree, and SVM, with both linear and non-linear kernels.
  • Conducted extensive experiments with diverse combinations of demographic data and gene-based data to determine their impact on the algorithms’ effectiveness in decision making.
  • Employed a rigorous process of algorithm tuning, including hyperparameter adjustments, to optimize model performance.
  • Achieved significant improvements in the models' ability to accurately assess cancer risks, with more than 90% sensitivity, demonstrating the potential for early detection and personalized patient care.

Tech Stack


Business Impact

Major Outcome

Higher Trust Factor: Cancer prediction ability is crucial business metric for our client & high level of sensitivity & specificity ensures higher amount of trust laid by end-users/ patients on our client’s diagnostic methods – leading to tangible business success.
Enhanced Patient Care: The improved sensitivity of the algorithms means that more patients at risk of cancer are identified early, leading to timely interventions and potentially life-saving treatments. This directly translates to better patient care and outcomes.
Cost Savings: Early detection and intervention can lead to cost savings for healthcare systems by reducing the need for expensive treatments for advanced-stage cancers. Additionally, by avoiding unnecessary treatments for low-risk individuals, resources can be allocated more efficiently.
Advancing Cancer Research: The client's enhanced DNA-based data analysis contributes to the advancement of cancer research by providing more accurate and detailed insights into cancer development and risk factors. This can lead to the development of new treatment strategies and further improvements in patient care.
Competitive Advantage: By implementing cutting-edge algorithms and techniques for DNA-based data analysis, the client establishes itself as a leader in personalized medicine and precision oncology. This will help in attracting more patients, seeking advanced and effective cancer treatments, further enhancing the client's reputation and market position.