br the rules were said to
the rules were said to be extracted. In the third step, the accuracy and coverage measure were evaluated for the extracted rules which results in the improved prediction accuracy. However, the prediction accuracy was achieved, but less focus was made on the error aspect.
Considering the aforementioned issues, in this Pirinixic Acid paper, a new combination approach for classifier ensembles using the Newton– Raphson’s MLMR preprocessing model is proposed, where the essential features are extracted to reduce the time for LCD diag-nosis. Newton–Raphson’s Maximum Likelihood model is applied to the MLMR attributes is proposed. Moreover, the first and the second derivative results of maximum relevance minimum redundant attributes are used to select the most relevant at-tributes. To achieve it, we explore the features of the MLMR model, Newton–Raphson’s Maximum Likelihood in the combina-tion process of an ensemble. Then, Boosted Weighted Optimized Neural Network Ensemble Classification algorithm is proposed to minimize the error (i.e., false positive error rate) and improve diagnosis accuracy. Optimized weights related to the decision of each ensemble classifier are defined dynamically, according to the ensemble classifier outputs and the relation among the outputs of all ensemble classifiers. In order to evaluate the feasi-bility of the proposed approach, an empirical analysis of ensem-ble performance using Thoracic Surgery Dataset, comparing its performance with ensemble classifier using traditional methods.
Healthcare is one of the essential sources in big data. Accurate analysis of healthcare data is highly in demand for diagnosing the disease at early stage. Recently, many research works have been designed for identifying disease in the big data with higher quality. But, there is a requirement for novel classification tech-nique to increase the diagnosis accuracy with time. Moreover, ML algorithms are designed to increase the prediction accuracy in big data. However, error rate still not exploited to its full poten-tial. Therefore, this research work motivates optimized machine learning algorithms to improve the diagnosis accuracy with lower time and error.
1.2. Research contributions
Contributions of this paper are as follows.
• To increase the performance of lung cancer diagnosis ac-curacy for big data as compared to state-of-the-art works, WONN-MLB method is used with Weight Optimized Neural Network to have Maximum Likelihood Boosting for Lung Cancer Disease.
• To minimize the classification time for early lung cancer disease diagnosis, integrated Newton–Raphsons MLMR pre-processing model is used to select the relevant attributes, to obtain higher diagnosis accuracy. • To reduce the error (i.e. false positive rate) and improves the disease diagnosis accuracy with higher classification ef-ficiency and lower classification time, Boosted Weighted Op-timized Neural Network Ensemble Classification algorithm is designed in WONN-MLB method.
LCD Section 2 describes the related works on various LCD diagnosis. In Section 3, the ensemble classification method along with a preprocessing model for LCD diagnosis is investigated, the maximum relevance method along with the maximum likelihood function is explored in detail, and the effects of the extracted
relevant attributes on the ensemble classification performance of the WONN-MLB method are also studied. In Section 4, the performance of the proposed approach is compared with the state-of-the-art approaches to demonstrate its effectiveness for LCD diagnosis and Section 5 concludes the paper.
2. Related works
With the invention of the microarray technique, scientists and researchers have immense opportunity to evaluate the expression levels of thousands of genes concurrently in a single experiment. In Ghorai et al. , the Nonparallel Plane Proximal Classifier (NPPC) was proposed for cancer classification in a Computer Aided Diagnosis (CAD) framework to ensure high classification accuracy and to minimize the computation time. But, Valvular heart disorders were considered to be one of the most difficult classification problems. Sengur et al.  used three powerful and popular ensemble learning representative called, bagging, boosting, and random subspaces to early detect Valvular heart disorders. However, the classification time was minimized us-ing methods, but the rate at which the accuracy was said to be attained remained unaddressed. In Costaa et al. , three Generalized Mixture (GM) functions were applied via dynamic weights to improve the classification accuracy of the classification system. Though the function handles single-label classification, multi-label classification problem was not addressed.
A case study for brain tumor diagnosis using global optimiza-tion based hybrid wrapper-filter feature selection with ensemble classification methods was proposed by Huda et al. . It in-creases the classification accuracy, but the classification time was not minimized. Approximately 40% of the world’s popu-lation is affected by cancer. A Proportion SVM was used by Huseeinet al.  for efficient categorization of Lung Nodules, which results in the improved diagnosing accuracy. The propor-tion of SVM failed to minimize the error rate in disease cat-egorization. Another method to early detect lung cancer was proposed by Abetiba et al.  using Radial Basis Function Neural Network with Affine Transforms which in turn achieved high classification accuracy and low mean square error. But, the per-formance of feature extraction was not improved. A review of feature selection and parallel classification systems was carried out by Jain et al.  to enhance the classification accuracy for disease perdition, but classification time was not minimized.