Disease Prediction Based on Individual's Medical History Using CNN

• Published in: 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) pp. 89 - 94
• Main Authors: Krishnamoorthy, Monica, Hameed, Mohammed Sharafath Abdul, Kopinski, Thomas, Schwung, Andreas
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2021
• Subjects: Convolutional Neural Network; Convolutional neural networks; Data Processing; Disease Prediction; Electronic Medical Records; Forecasting; History; Machine learning; Machine learning algorithms; Prediction algorithms; Predictive models
• DOI: 10.1109/ICMLA52953.2021.00022

Analyzing health records to avert future complications and provide the right treatment plays an important role in medical diagnostics. This paper introduces a method to use real-life ICD-coded Electronic Medical Records (EMR), clinical data collected between 2018-2019 in Africa, to create a prediction model. The prediction model uses recent advances in machine learning, specifically Convolutional Neural Networks (CNN), to provide an alternative and a powerful way for disease risk prediction. An algorithm is first designed to process the real-life EMR data into multi-step time-series forecasting data. A CNN model is then developed, to predict the individual's future disease class (ICD-10 CM chapters) risk based on their demographic and medical history. The experimental results show that the model predicts future disease risk, for an individual, with an accuracy of 80.73%. We conclude that such a model can prove immensely useful in cost savings for individuals and hospitals by performing preemptive corrective action, provide additional guidelines for hospitals' capacity planning, when the model is applied for demography, and, most importantly, peace of mind on personal health.