Machine Learning for Healthcare Applications

We have developed two models in this chapter based on the two popular machine learning algorithms which are Decision tree and Random forest and tested both the models based on the synthetic dataset. We have developed a web-based application to demonstrate the models proposed in this chapter. A few screenshots of the application shown in Figure 2.2.

To analyze the effectiveness and the performance of the model proposed in this chapter, we used the standard performance metrics [13] and [3] accuracy, precision, recall, and F1-score.

The accuracy of the model is calculated using the equation given below.

Table 2.2shows the accuracy of the model for the decision tree proposed in this chapter.

Figure 2.3shows the accuracy comparison between the two models which are proposed in this chapter and it is observed the model-II gives more accuracy than the model-I.

Figure 2.2 Screenshots of the web application.

Table 2.2 Accuracy of the model

Figure 2.3 Accuracy: Model-I vs Model-II.

The precision of the model is calculated using the equation given below.

Figure 2.4shows the precision comparison between the two models which are proposed in this chapter and it is observed the model-II gives more accuracy than the model-I. Table 2.3shows the Precision comparison between the model-1 and model-2.

The recall of the model is calculated using the equation given below.

Figure 2.4 Precision: Model-I vs Model-II.

Table 2.3 Precision of the model.

Table 2.4 Recall of the model.

Figure 2.5 Recall: Model-I vs Model-II.

Table 2.4shows the Recall comparison between the model-1 and model-2.

Figure 2.5shows the Recall comparison between the two models which are proposed in this chapter and it is observed the model-II gives more accuracy than the model-I.

The F1-score is the harmonic mean of precision and recall. Below equation used to calculate the F1-score.

Figure 2.6shows the F1-score comparison between the two models which are proposed in this chapter and it is observed the model-II gives more accuracy than the model-I. Table 2.5shows the F1-Score comparison between the model-1 and model-2.

Table 2.5 F1-score of the model.

Figure 2.6 Recall: Model-I vs Model-II.

In this chapter, we have proposed an architecture based on machine learning algorithms. Basically, we focus on a challenging problem of predicting the overall health status of an individual based on their daily life activities and measures. The proposed system predicts the overall health status of a person and future diseases using machine learning techniques. To demonstrate the proposed model, we have created a web-based application. The proposed model helps the user to understand their health status by submitting their details. For training and testing we used the synthetic data, in the future we need to test the proposed model using the real data by collecting from the users. In this work, we attempted a general healthcare problem and a lot more has to be done in the future. The future work is to predict the diseases based on the overall health status estimation using the models proposed in this chapter.