Development of ML models for a medical center
Development of two machine learning models for use in a medical center
Project definition
Location
North America
Client
Private clinic in North America
Industry
Healthcare
Project type
ML model
Service list
ML Engineering / Computer Vision
Problem
The goal of this project was to develop two machine learning models for use in a medical center. The first model is designed to predict the likelihood of patient churn, allowing the clinic to take timely measures to retain clients. The model was trained using anonymized data on patient visits, the duration of their stay at the center, the number of consultations with doctors, and other related factors. The second model was designed to automate the handling of patients paper documentation. It was intended to scan, digitize, and process information extracted from physical documents
Solution
Both models were successfully developed and implemented in practice. The first model demonstrates a client churn prediction accuracy of 78%. The generated data is forwarded to the quality control department, where specialists analyze the reasons why clients may have discontinued services and switched to competitors.
- Python
- Tensorflow
- pandas
- scikit learn
- Mlflow
Project highlights
Highlights of ML model development
01
2 ML models
We developed two ML models for the medical center
02
Accuracy of customer behavior prediction
The accuracy of customer churn prediction is 78%
03
Accuracy of document recognition
The accuracy of medical paper document recognition is 99%
Technologies
Python
Tensorflow
pandas
scikit learn
Mlflow
Our workflow
Development Process
In FTECH-IT we have a cohesive team of professionals across various IT fields, enabling us to handle projects end-to-end without needing external specialists. No matter how complex the project is, all you need to provide is the idea — FTECH-IT will take care of everything else
Development of ML models for a medical center
Development of two machine learning models for use in a medical center
