Predicting Degenerative Arthritis Using AI Data Analytics | RapidMiner

Results

• Performed data cleaning, organization, and splitting of patients’ clinical information to filter out usable data.

• Used RapidMiner to build an AI prediction model capable of forecasting joint deterioration over the next 2 and 3 years.

Highlights

• Richin developed custom Python scripts to integrate data and automatically organize information and rapidly progressing cases.

• Used RapidMiner and the Naive Bayes method to build a prediction model for the progression of osteoarthritis.

• Based on validation results, the model achieved an accuracy of around 80% when predicting disease progression over the next 2 and 3 years.

Extended Applications

       The AI model is not limited to osteoarthritis. With appropriate model adjustments and retraining on new datasets, it can also be applied to other medical fields, such as:

• Cancer early prediction: By analyzing patient genetic data and lifestyle habits, AI can detect potential cancer risks at an early stage and help physicians formulate timely treatment plans.

• Diabetes progression prediction: Using data such as blood glucose, body weight, diet, and exercise habits, AI models can predict how diabetes will develop and help patients adjust their lifestyle.

• Cardiovascular disease risk analysis: By leveraging ECG data, blood pressure, and exercise habits, AI can effectively predict future cardiovascular risk and support physicians in early intervention and treatment.

Conclusion

       As AI technology continues to advance in the medical field, the application scope of AI prediction models will become even broader—from early disease prediction to the development of personalized treatment plans, AI will play a key role in future healthcare.