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The findings of this work provided extensive understanding of the computational parameters that can be used with k-means. The results indicated that k-means has a potential to classify BCW dataset.

BackgroundOwing to the use of tobacco and the consumption of alcohol and adulterated food, worldwide cancer incidence is increasing at an alarming and frightening rate. Since the last decade of the twentieth century, lung cancer has been the most common cancer type. This study aimed to determine the global status of lung cancer and to evaluate the use of computational methods in the early detection of lung cancer.MethodsWe used lung cancer data from the United Kingdom (UK), the United States (US), India, and Egypt. For statistical analysis, we used incidence and mortality as well as survival rates to better understand the critical state of lung cancer.ResultsIn the UK and the US, we found a significant decrease in lung cancer mortalities in the period of 1990–2014, whereas, in India and Egypt, such a decrease was not much promising. Additionally, we observed that, in the UK and the US, the survival rates of women with lung cancer were higher than those of men. We observed that the data mining and evolutionary algorithms were efficient in lung cancer detection.ConclusionsOur findings provide an inclusive understanding of the incidences, mortalities, and survival rates of lung cancer in the UK, the US, India, and Egypt. The combined use of data mining and evolutionary algorithm can be efficient in lung cancer detection.

Breast cancer is one of the most common forms of cancer having a worldwide prevalence. Continuous research is going on for detecting breast cancer in its early stage as the possibility of cure is very high in the early stage. The two main objectives of this work were: firstly, to compare the performance of k-means and fuzzy c-means (FCM) clustering algorithms; and secondly, to make an attempt to carefully consider and examine, from multiple points of view, the combination of different computational measures for k-means and FCM algorithms for a potential to achieve better clustering accuracy. K-means and FCM algorithms have been considered to understand the impact of clustering on the breast cancer data. The execution of k-means algorithm is based on centroid, distance, split method, threshold, epoch, attributes, and number of iterations; while FCM is executed on the basis of fuzziness value and termination condition. The breast cancer Wisconsin (BCW) dataset was used for the experimentation and the comparison. The combination of variance and same centroid offers better outcome in terms of k-means algorithm. The highest and lowest clustering accuracies are (94.7%, 77.1%) and (94.4%, 88.5%) for foggy and random centroid, respectively. The overall average positive prediction accuracy obtained by this approach is approximately 92%. In case of FCM, the highest and lowest clustering accuracies are (97.2%, 91.1%), (97.2%, 90.9%), (97.8%, 90.4%), and (97.1%, 90.2%) for different combination of fuzziness and termination criteria. The average highest and lowest clustering accuracies are (95.7%, 94.7%), (95.9%, 93.6%), (95.3%, 94.2%), and (95.6%, 93.7%) for the same combination in the case of FCM. K-means algorithm was more prominent and consistent in terms of computation time as FCM required more time to carry out several fuzzy calculations and iterations. The findings of this work provide an incisive and extensive understanding of the computational parameters used with k-means and FCM algorithms. The computational results indicate that FCM algorithm was found to be prominent and consistent than k-means algorithm when executed with different iterations, fuzziness values, and termination criteria. It is more potentially capable in clustering BCW dataset as the clustering accuracy is more important than time.

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