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. 2021 Apr 7;11(1):7567.
doi: 10.1038/s41598-021-87171-5.

Development of machine learning model for diagnostic disease prediction based on laboratory tests

Dong Jin Park  1 , Min Woo Park  2 , Homin Lee  3 , Young-Jin Kim  4 , Yeongsic Kim  5 , Young Hoon Park  6
Affiliations

Development of machine learning model for diagnostic disease prediction based on laboratory tests

Dong Jin Park et al. Sci Rep. .

Abstract

The use of deep learning and machine learning (ML) in medical science is increasing, particularly in the visual, audio, and language data fields. We aimed to build a new optimized ensemble model by blending a DNN (deep neural network) model with two ML models for disease prediction using laboratory test results. 86 attributes (laboratory tests) were selected from datasets based on value counts, clinical importance-related features, and missing values. We collected sample datasets on 5145 cases, including 326,686 laboratory test results. We investigated a total of 39 specific diseases based on the International Classification of Diseases, 10th revision (ICD-10) codes. These datasets were used to construct light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost) ML models and a DNN model using TensorFlow. The optimized ensemble model achieved an F1-score of 81% and prediction accuracy of 92% for the five most common diseases. The deep learning and ML models showed differences in predictive power and disease classification patterns. We used a confusion matrix and analyzed feature importance using the SHAP value method. Our new ML model achieved high efficiency of disease prediction through classification of diseases. This study will be useful in the prediction and diagnosis of diseases.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Confusion matrix of the ensemble model (optimal accuracy model): (A) The predictive power (accuracy) of TOP1 (representing the most likely disease) result, (B) TOP5 (the five most likely diseases) result.
Figure 2
Figure 2
The mean SHAP method result between parameters and disease classifications in LightGBM.
Figure 3
Figure 3
The mean SHAP method result between parameters and disease classifications in XGBoost.
Figure 4
Figure 4
Overall framework of DPMLT. The development of DPMLT methodology involved for three major steps. (1) Data collection and preprocessing (2) Model selection, training and ensemble modeling (3) Performance evaluation.
See this image and copyright information in PMC

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