The combination of machine learning (ML) approaches in healthcare is a massive advantage designed at curing illness of millions of persons. Several efforts are used by researchers for detecting and providing primary phase insights as to cancer analysis. Lung cancer remained the essential source of disease connected mortality for both men as well as women and their frequency was increasing around the world. Lung disease is the unrestrained progress of irregular cells which begin off in one or both Lungs. The previous detection of cancer is not simpler procedure however if it can be detected, it can be curable, also finding the survival rate is a major challenging task. This study develops an Ant lion Optimization (ALO) with Deep Belief Network (DBN) for Lung Cancer Detection and Classification with survival rate prediction. The proposed model aims to identify and classify the presence of lung cancer. Initially, the proposed model undergoes min-max data normalization approach to preprocess the input data. Besides, the ALO algorithm gets executed to choose an optimal subset of features. In addition, the DBN model receives the chosen features and performs lung cancer classification. Finally, the optimizer is utilized for hyperparameter optimization of the DBN model. In order to report the enhanced performance of the proposed model, a wide-ranging experimental analysis is performed and the results reported the supremacy of the proposed model.

Lung cancer (LC) is the major and primary reason for cancer death in both men and women. Demonstration of LC in the body parts of the patient exposes via earlier symptoms in several persons [

Every type of LC develops and spreads in different means and it is treated accordingly [

In [

This study develops a Ant lion Optimization with Optimal Deep Belief Network (ODBN) model named ALO-ODBN for Lung Cancer Detection and Classification. Initially, the ALO-ODBN model undergoes min-max data normalization approach to preprocess the input data. Besides, the ALO algorithm gets executed to choose an optimal subset of features. In addition, the DBN model receives the chosen features and performs lung cancer classification. Finally, the Adam optimizer is utilized for hyperparameter optimization of the DBN model. In order to report the enhanced performance of the ALO-ODBN model, a wide-ranging experimental analysis is performed and the results reported the supremacy of the ALO-ODBN model.

In this study, a new ALO-ODBN model has been developed for Lung Cancer Detection and Classification. Initially, the ALO-ODBN model undergoes min-max data normalization approach to preprocess the input data. Besides, the ALO algorithm gets executed to choose an optimal subset of features. In addition, the DBN model receives the chosen features and performs lung cancer classification. Finally, the Adam optimizer is utilized for hyperparameter optimization of the DBN model.

Initially, the ALO-ODBN model undergoes min-max data normalization approach to pre-process the input data. In any ML model, data normalization is widely utilized to attain proficient results [

The recent meta heuristic method of ant lion optimization algorithm is the interaction of antlions and ants. mall cone shaped traps are made by the antlions, in which they hide and wait for their prey. The major steps involved in the tuning process are illustrated below with the help of steps given below

The ant lion optimization algorithm simulates the hunting mechanism of the antlions. The following subsections describe the steps of the algorithm.

The random walk of ants, where,

The fitness values for the ants are stored in the form of a matrix and are a function of the objective function. In the same manner the fitness values of the antlions are also stored in another matrix. During each step f the iteration, the random walk of the ants is to be confined within the boundary of the search space. This is executed by

The ants randomly walk without seeing the trap, so, it may fall down into the cone shaped trap. This is realized by adaptively decreasing the radius of the random walk as shown.

Here,

Trapping of ant by antlion

The range of the random walk of the

At this stage, the DBN model receives the chosen features and performs lung cancer classification. Typically, DBN is constructed by stacking Restricted Boltzmann Machine (RBM) that captures higher-order correlation that is noted in the visible unit. DBN is pretrained in an unsupervised greedy layer-wise manner for learning a stack of RBM through the Contrastive Divergence (CD) approach. The output depiction of RBM is utilized as the input dataset to train the RBM in the stack. Afterward the pretraining, the DBN is finetuned by BP of error derivative and the initial weight and bias of all the layers are corrected. RBM is an approach to represent each training sample in a compact and meaningful manner, by capturing the regularities and inner structure. This is realized by presenting an additional set of neurons named hidden unit to the network that value is indirectly fixed from training dataset [

Consider the input dataset and the reconstruction is standard curve of distinct shapes. The aim is to reduce the error or diverging area in the two curves, named Kullback-Leibler

Therefore, the possibility distribution of a group of novel input

Consider

Now,

For good understanding of the CD approach, mathematical calculation is included.

Now

Now

Next, the joint possibility through

Assume

The equation to update weight is estimated by taking derivative of

Here

The conditional probability of

Now,

Having

Now, recon represents the recreation stage.

Finally, the Adam optimizer is utilized for hyperparameter optimization of the DBN model. Simultaneously, the hyperparameter optimization of the DBN was performed by Adam optimizer. It is applied for estimating an adaptive learning value where the variable is employed for training the variable of the DNN [

The next momentum is expressed by,

Here

This section inspects the performance validation of the ALO-ODBN model using benchmark lung cancer dataset [

Class labels | Accuracy | Precision | Recall | F-score |
---|---|---|---|---|

Class 1 | 100.00 | 100.00 | 100.00 | 100.00 |

Class 2 | 96.88 | 92.86 | 100.00 | 96.30 |

Class 3 | 96.88 | 100.00 | 90.00 | 94.74 |

Class 1 | 96.88 | 90.00 | 100.00 | 94.74 |

Class 2 | 96.88 | 100.00 | 92.31 | 96.00 |

Class 3 | 100.00 | 100.00 | 100.00 | 100.00 |

Class 1 | 93.75 | 81.82 | 100.00 | 90.00 |

Class 2 | 96.88 | 100.00 | 92.31 | 96.00 |

Class 3 | 96.88 | 100.00 | 90.00 | 94.74 |

Class 1 | 90.62 | 75.00 | 100.00 | 85.71 |

Class 2 | 93.75 | 100.00 | 84.62 | 91.67 |

Class 3 | 96.88 | 100.00 | 90.00 | 94.74 |

Class 1 | 93.75 | 81.82 | 100.00 | 90.00 |

Class 2 | 100.00 | 100.00 | 100.00 | 100.00 |

Class 3 | 93.75 | 100.00 | 80.00 | 88.89 |

The training accuracy (TA) and validation accuracy (VA) attained by the ALO-ODBN approach on lung cancer classification is demonstrated in

The training loss (TL) and validation loss (VL) achieved by the ALO-ODBN model on lung cancer classification are established in

A brief precision-recall examination of the ALO-ODBN model on test dataset is represented in

A brief ROC investigation of the ALO-ODBN method on test dataset is depicted in

For ensuring the betterment of the ALO-ODBN model, a comparison study is made with existing models in

Methods | Accuracy | Precision | Recall | F1-score |
---|---|---|---|---|

ALO-ODBN | 97.92 | 97.62 | 96.67 | 97.01 |

DNN Model | 97.16 | 90.87 | 89.23 | 81.30 |

SVM algorithm | 70.56 | 88.34 | 87.03 | 85.60 |

RBF neural network | 95.83 | 90.15 | 88.48 | 92.15 |

Decision tree (C4.5) | 96.96 | 93.85 | 93.12 | 94.64 |

A brief

From the above mentioned tables and figures, it is apparent that the ALO-ODBN model has outperformed the other methods interms of different measures. The overall survival rate detection of patient is improved when compared with other existing methods.

In this study, a new ALO-ODBN model has been developed for Lung Cancer Detection, Classification and survival rate prediction. The proposed ALO-ODBN model aims to identify and classify the presence of lung cancer. Initially, the ALO-ODBN model undergoes min-max data normalization approach to preprocess the input data. Besides, the ALO algorithm gets executed to choose an optimal subset of features. In addition, the DBN model receives the chosen features and performs lung cancer classification. Finally, the Adam optimizer is utilized for hyperparameter optimization of the DBN model. In order to report the enhanced performance of the ALO-ODBN model, a wide-ranging experimental analysis is performed and the results reported the supremacy of the ALO-ODBN model.