Field of the Invention
The present invention relates to an automated pancreatic cancer detection system using urinary
biomarkers and machine learning algorithms. More specifically, it focuses on a non-invasive
diagnostic tool that processes biomarker data through deep learning and statistical models to
classify patient conditions accurately.
Background of the Invention
Pancreatic cancer is a highly lethal disease, with most cases diagnosed at advanced stages due to
a lack of early symptoms. Traditional diagnostic methods, such as CT scans, MRis, and biopsies,
are expensive, invasive, and often performed too late. Urinary biomarkers have emerged as a
promising alternative for non-invasive screening, offering early indications of pancreatic
malignancy. However, manual analysis of biomarker data can be inconsistent and subject to human
error.
Current research integrates machine learning and deep learning to analyze biomarker levels and
improve early detection. While some existing models use CA19-9, our system exclusively utilizes
urinary biomarkers (LYVEl, REGlB, TFFl, REGlA, and Creatinine) for a fully non-invasive
approach. The proposed system leverages lD-CNN, LSTM, and Random Forest models to enhance
classification accuracy and clinical applicability.
Summary of the Invention
The disclosed system automates the analysis of urinary biomarkers for pancreatic cancer
detection using machine learning models. The system consists of two core modules:
• . Data Preprocessing Module - Normalizes biomarker concentrations, handles missing
values, and prepares data for model input.
• Classification Module - Implements lD-CNN, LSTM, and Random Forest to analyze
biomarker levels and classify patients into one of three categories: Healthy, Non-Cancerous
Pancreatic Condition, or PDAC.
The workflow includes:
1. Feature extraction and normalization of biomarker data.
2. Model training using deep learning architectures to enhance pattern recognition.
3. Multi-class classification to differentiate between healthy individuals, benign conditions,
and PDAC.
4. Performance evaluation using metrics such as accuracy (92.8%), precision (91.5%), recall
(90.7%), and AUC-ROC (0.94).
This system significantly improves diagnostic efficiency, reduces false positives/negatives, and
supports early-stage pancreatic cancer screening.
Detailed Description of the Invention
The system follows a structured process to ensure high accuracy and efficiency in predicting
pancreatic cancer:
I. Data Acquisition: Urinary biomarker data is collected from patients and stored in a
structured format.
2. Data Preprocessing:
o Normalization: Adjusts biomarker levels based on creatinine concentration.
o Handling Missing Data: Uses mean imputation and KNN imputation to ensure
data completeness.
3. Feature Engineering & Model Training:
o I D-CNN extracts spatial features from biomarker sequences.
o LSTM captures temporal dependencies in biomarker trends.
o Random Forest provides an interpretable ensemble learning approach.
4. Classification & Prediction:
o Models predict whether the patient is Healthy, has a Benign Pancreatic
Condition, or has PDAC.
5. Evaluation & Validation:
o Performance is validated using 5-fold cross-validation.
o Metrics such as accuracy (92.8%) and AUC-ROC (0.94) are used to assess
reliability.
Advantages of the Invention
• Non-Invasive Detection: Uses only urinary biomarkers, eliminating the need for invasive
biopsies.
• High Accuracy: Achieves 92.8% classification. accuracy with lD-CNN outperforming
traditional·models.
• Fast & Efficient: Reduces the time required for biomarker-based diagnosis.
• Scalable: Can be integrated into hospital diagnostic systems for large-scale screening .
• Automated & Objective: Minimizes_ human errors and ensures consistent evaluation.

CLAIMS
1. A machine learning-based system for pancreatic cancer prediction using urinary
biomarkers, comprising:
a. A data preprocessing module for biomarker normalization and feature
extraction.
b. A classification module implementing 10-CNN, LSTM, and Random Forest
models for patient classification.
c. A performance evaluation module assessing accuracy, precision, recall, and AUCROC.
2. A reporting system that .provides diagnostic recommendations to healthcare
professionals.
3. The system as claimed in Claim 1, wherein the classification module uses deep learning
models to improve biomarker-based prediction.
4. The system as claimed in Claim 1, wherein the preprocessing module applies mean
normalization and KNN imputation for missing data.
5. The system as claimed in Claim 1, wherein model evaluation is· performed using 5-fold
cross-validation to ensure robustness.
6. A method for pancreatic cancer prediction, comprising:
a. Receiving patient urinary biomarker data.
b. Preprocessing the data through normalization and feature engineering.
c. Feeding the data into 1D CNN, LSTM, ond llondom Forest models for
classification.
d. Assigning a diagnostic category (Healthy, Non-Cancerous Pancreatic Condition,
orPDAC).
e. Generating an automated diagnostic report with risk assessment.
CONCLUSION
This invention introduces an automated pancreatic cancer detection system leveraging machine
learning and urinary biomarkers to enhance 'early diagnosis. By employing deep learning models
like 10-CNN and LSTM, the system significantly improves classification accuracy while
maintaining a non-invasive, cost-effective, and scalable approach. The integration of urinary
biomarker analysis withAl-driven classification provides a clinically relevant tool for early-stage
pancreatic cancer detection, ultimately supporting better patient outcomes .