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In recent years, due to an increase in the incidence of different cancers, various data sources are available in this field. Consequently, many researchers have become interested in the discovery of useful knowledge from available data to assist faster decision-making by doctors and reduce the negative consequences of such diseases. Data mining includes a set of useful techniques in the discovery of knowledge from the data: detecting hidden patterns and finding unknown relations. However, these techniques face several challenges with real-world data. Particularly, dealing with inconsistencies, errors, noise, and missing values requires appropriate preprocessing and data preparation procedures. In this article, we investigate the impact of preprocessing to provide high-quality data for classification techniques. A wide range of preprocessing and data preparation methods are studied, and a set of preprocessing steps was leveraged to obtain appropriate classification results. The preprocessing is done on a real-world breast cancer dataset of the Reza Radiation Oncology Center in Mashhad with various features and a great percentage of null values, and the results are reported in this article. To evaluate the impact of the preprocessing steps on the results of classification algorithms, this case study was divided into the following 3 experiments: Breast cancer recurrence prediction without data preprocessing Breast cancer recurrence prediction by error removal Breast cancer recurrence prediction by error removal and filling null values Then, in each experiment, dimensionality reduction techniques are used to select a suitable subset of features for the problem at hand. Breast cancer recurrence prediction models are constructed using the 3 widely used classification algorithms, namely, naïve Bayes, k-nearest neighbor, and sequential minimal optimization. The evaluation of the experiments is done in terms of accuracy, sensitivity, F-measure, precision, and G-mean measures. Our results show that recurrence prediction is significantly improved after data preprocessing, especially in terms of sensitivity, F-measure, precision, and G-mean measures.

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The epidemic threshold of a social system is the ratio of infection and recovery rate above which a disease spreading in it becomes an epidemic. In the absence of pharmaceutical interventions (i.e. vaccines), the only way to control a given disease is to move this threshold by non-pharmaceutical interventions like social distancing, past the epidemic threshold corresponding to the disease, thereby tipping the system from epidemic into a non-epidemic regime. Modeling the disease as a spreading process on a social graph, social distancing can be modeled by removing some of the graphs links. It has been conjectured that the largest eigenvalue of the adjacency matrix of the resulting graph corresponds to the systems epidemic threshold. Here we use a Markov chain Monte Carlo (MCMC) method to study those link removals that do well at reducing the largest eigenvalue of the adjacency matrix. The MCMC method generates samples from the relative canonical network ensemble with a defined expectation value of $$\lambda _{\text {max}}$$ λ max . We call this the “well-controlling network ensemble” (WCNE) and compare its structure to randomly thinned networks with the same link density. We observe that networks in the WCNE tend to be more homogeneous in the degree distribution and use this insight to define two ad-hoc removal strategies, which also substantially reduce the largest eigenvalue. A targeted removal of 80% of links can be as effective as a random removal of 90%, leaving individuals with twice as many contacts. Finally, by simulating epidemic spreading via either an SIS or an SIR model on network ensembles created with different link removal strategies (random, WCNE, or degree-homogenizing), we show that tipping from an epidemic to a non-epidemic state happens at a larger critical ratio between infection rate and recovery rate for WCNE and degree-homogenized networks than for those obtained by random removals.