Symptom-based drug prediction of lifestyle-related chronic diseases using unsupervised machine learning techniques
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View abstract on PubMed
Summary
This summary is machine-generated.This study developed an unsupervised machine learning tool to predict drugs for lifestyle-related diseases (LSDs) based on symptoms. The web application aids clinicians in early treatment decisions for heart and lung conditions.
Area Of Science
- Computational biology
- Bioinformatics
- Machine learning in healthcare
Background
- Lifestyle-related diseases (LSDs) present a significant economic and health burden, often affecting the heart and lungs.
- Early treatment of LSDs is crucial, and symptom-based information is the primary data available to clinicians.
- Developing predictive models for drug discovery in LSDs is essential for timely therapeutic interventions.
Purpose Of The Study
- To apply unsupervised machine learning (ML) techniques for predicting drugs from symptoms in lifestyle-related diseases (LSDs).
- To focus specifically on developing predictive models for pulmonary and heart diseases, a subset of LSDs.
- To create a user-friendly web application for symptom-based drug prediction.
Main Methods
- Utilized drug-disease, disease-symptom associations for 143 LSDs, 1271 drugs, and 305 symptoms to compute direct drug-symptom associations.
- Developed and compared four ML clustering algorithms (K-Means, Bisecting K-Means, Mean Shift, BIRCH) to group drugs based on symptom features.
- Saved the optimal ML model and developed a web application for drug prediction from input symptoms.
Main Results
- The Bisecting K-means model demonstrated superior performance, achieving a silhouette coefficient of 0.647 and generating 138 distinct drug clusters.
- Drugs within identified clusters exhibited significant similarity based on gene ontology, chemical ontology, and maximum common substructure analyses.
- The web application provides a confidence score for predicted drugs, enhancing its utility for clinical decision support.
Conclusions
- Direct drug-symptom associations were computed and leveraged to create a novel, unsupervised ML-based tool for predicting drugs for LSDs.
- This ML-driven prediction tool can serve as a valuable second opinion for clinicians, facilitating earlier treatment initiation for LSD patients.
- A publicly accessible web application (http://bicresources.jcbose.ac.in/ssaha4/sdldpred) offers a simple interface for end-users to utilize the ML-based drug prediction tool.
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