Accurate surgery time prediction (ASTP) strategy based on artificial intelligence techniques

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Our strategy consists of two layers as illustrated in Fig. 1. The preprocessing layer performs Mixed-Scaling, computes feature importance using LSTM and Random Forest, and Feature ranking, which orders the features and selects the most informative subset. The prediction layer applies HGBR to the ranked features, yielding the final time-to-surgery prediction.

Preprocessing layer in ASTP

In this layer, the data is processed using One Hot encoding, Mixed-Scaling, and determining the importance and ranking of features.

One hot encoding

In tabular data modeling, each categorical value in Eq. 1 is represented by a one-hot vector of length K (i.e., a (:times:) K), where K is the number of possible classes for that categories20. The vector contains zeros in all cells except one with the value 1, which specifies the actual class.

$$:{left[varnothing::right(xleft)right]}_{k}=1:left{x={c}_{k}right},:::::::::::k=1,dots:..,K,::::::::::::::sum:_{k}^{K}{left[varnothing::right(xleft)right]}_{k}=1$$

(1)

(:c={:{c}_{1},dots:dots:.,{c}_{k}}) Is the category set (with size K), x (:epsilon) c is observed category, (:{left[{varnothing}:right(xleft)right]}_{k}:)denotes the K-th coordinate of the one-hot vector, 1{⋅} is the indicator function (1 if the condition holds, 0 otherwise), n is the number of samples, the one hot block is O (:epsilon) (:{left{text{0,1}right}}^{ntimes:k}) in Eq. 2.

$$:{O}_{ik}=1:{:{x}_{i}=:{c}_{k}}$$

(2)

This formulation ensures that the learning model does not assume that “larger numbers are more important,” because simple numerical notations (e.g., “stable = 1, more stable = 2, unstable = 3”) might imply an unreal rank relationship or distance between classes. For example, the value “emerging” is not “more important” than “scheduled” because we write it as 2…

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