Machine Learning for Healthcare Applications. Группа авторов

15 dangerous smoking status Drink 0 if the number of units consumed is 0 drinking status is good 1 if gender is male and the number of units consumed is less than 2 If gender is female and the number of units consumed is less than 1 drinking status is reasonable 2 if gender is male and the number of units consumed is between 3 and 4 If gender is female and the number of units consumed is less than 2 and 3 drinking status is bad

      2.4.5 Pre-Processing

       BMR = (10 × Weight in kg) + (6.25 × Height in cm) − (5 × Age in years) + 5 ---------[4]

       BMR = 10 × 63 + 6.25 × 176 − 5 × 21 + 5 = 1630

       Calories needs to be consumed = BMR × Physical Activity = 1630 × 1.375 = 2241.25

       Calorie Difference = Calories consumed − Calories needs to be consumed = 1,800 − 2241.25 = −441.25.

      Thus, inputs after pre-processing are:

      Input1 = (Age = 21) ∩ (Gender = Male) ∩ (No. of cigars smoked = 0) ∩ (Units of Alcohol Consumed = 2) ∩ (Screen Time = 6) ∩ (Sleep Time = 8) ∩ (Calorie Difference = −441.25).

2.5 Experimental Results

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.

      2.5.1 Performance Metrics

      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.

       2.5.1.1 Accuracy

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

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

Figure 2.3 shows 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

Health status	Model 1	Model 2
Accuracy: Phase-I	Accuracy: Phase-II	Accuracy: Phase-I	Accuracy: Phase-II
Sleep	90.54	93.64	91.54	94.64
Smoke	92.21	94.01	94.21	96.01
Drink	94.63	95.99	96.63	97.99
Screen	93.11	94.76	94.11	95.76
Calories	94.00	97.83	95.00	98.83

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

       2.5.1.2 Precision

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

Figure 2.4 shows 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 2.3 shows the Precision comparison between the model-1 and model-2.

       2.5.1.3 Recall

      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.

Health status	Model 1	Model 2
Precision:Phase-I	Precision:Phase-II	Precision:Phase-I	Precision:Phase-II
Sleep	95.5555556	97.826087	95.6043956	97.8723404
Smoke	95.6989247	95.8333333