text: Inverse classification is the process of manipulating an instance such that it is more likely to conform to a specific class. Past methods that address such a problem have shortcomings. Greedy methods make changes that are overly radical, often relying on data that is strictly discrete. Other methods rely on certain data points, the presence of which cannot be guaranteed. In this paper we propose a general framework and method that overcomes these and other limitations. The formulation of our method uses any differentiable classification function. We demonstrate the method by using Gaussian kernel SVMs. We constrain the inverse classification to occur on features that can actually be changed, each of which incurs an individual cost. We further subject such changes to fall within a certain level of cumulative change (budget). Our framework can also accommodate the estimation of features whose values change as a consequence of actions taken (indirectly changeable features). Furthermore, we propose two methods for specifying feature-value ranges that result in different algorithmic behavior. We apply our method, and a proposed sensitivity analysis-based benchmark method, to two freely available datasets: Student Performance, from the UCI Machine Learning Repository and a real-world cardiovascular disease dataset. The results obtained demonstrate the validity and benefits of our framework and method.
  @cite_4: We propose a methodology that uses data envelopment analysis (DEA) for solving the inverse classification problem. An inverse classification problem involves finding out how predictor attributes of a case can be changed so that the case can be classified into a different and more desirable class. For a binary classification problem and non-negative decision-making attributes, we show that under the assumption of conditional monotonicity, and convexity of classes, DEA can be used for inverse classification problem. We illustrate the application of our proposed methodology on a hypothetical and a real-life bankruptcy prediction data.
  @cite_7: Support vector machines are a valuable tool for making classifications, but their black-box nature means that they lack the natural explanatory value that many other classifiers possess. Alternatively, many popular websites have shown recent success in explaining recommendations based on behavior of other users. Inspired by these ideas, we suggest two novel methods for providing insight into local classifications produced by a support vector machine. In the first, we report the support vectors most influential in the final classification for a particular test point. In the second, we determine which features of that test point would need to be altered (and by how much) in order to be placed on the separating surface between the two classifications. We call the latter technique “border classification.” In addition to introducing these explanatory techniques, we also present a free-for-download software tool that enables users to visualize these insights graphically.
  @cite_23: Graphical abstractDisplay Omitted Highlights? The expert system is constructed by supervised learning approach and optimization. ? The system predicts 10-year CVD risks based on cardiovascular risk information. ? The lifestyle option with the lowest CVD risk varies based on individuals. ? The system prioritizes lifestyle options based risks, limitations, and preferences. We propose a proof-of-concept machine-learning expert system that learned knowledge of lifestyle and the associated 10-year cardiovascular disease (CVD) risks from individual-level data (i.e., Atherosclerosis Risk in Communities Study, ARIC). The expert system prioritizes lifestyle options and identifies the one that maximally reduce an individual's 10-year CVD risk by (1) using the knowledge learned from the ARIC data and (2) communicating for patient-specific cardiovascular risk information and personal limitations and preferences (as defined by variables used in this study). As a result, the optimal lifestyle is not only prioritized based on an individual's characteristics but is also relevant to personal circumstances.We also explored probable uses and tested the system in several examples using real-world scenarios and patient preferences. For example, the system identifies the most effective lifestyle activities as the starting point for an individual's behavior change, shows different levels of BMI changes and the associated CVD risk reductions to encourage weight loss, identifies whether weight loss or smoking cessation is the most urgent change for a diabetes patient, etc. Answers to the questions noted above vary based on an individual's characteristics. Our validation results from clinical trial simulations, which compared original with the optimal lifestyle using an independent dataset, show that the optimal individualized patient-centered lifestyle consistently reduced 10-year CVD risks.
  @cite_2: 
  @cite_5: Cardiovascular diseases (CVD) remain the leading cause of death around the world. In past decades, many preventive strategies have been recommended to reduce the risk of CVD. However, current CVD risk prediction schemes are not targeted to personalized and optimized recommendations. The goal of this study was to better identify individuals at high risk of a CVD event, and recommend an optimal set of risk factor changes that could reduce the risk of long-term CVD events. We identified 100 demographic, lab, lifestyle, and medication variables for 12907 individuals who participated to the ARIC study and had no CVD events at baseline. We examined the prognostic performance of these features in isolation and ranked them based on mutual information. Then we combined those features to build predictive models using k-nearest neighbor prediction to estimate the 10-year CVD risk for each individual. Our feature-ranking method agreed with traditional risk factors identified by a domain expert. Our approach was successful in identifying cases with high risk and performed as well as traditional methods. Then we applied inverse classification to find the personalized optimal changes to reduce 10-year CVD risk. We also created a personalized package of five optimal changes for each individual to reduce their 10-year CVD risk. This approach can be applied to other chronic disease risk prediction and personalized recommendations, and may be useful to both health care providers and patients in making personalized health care recommendations and decisions.
  @cite_11: 
summary: Past works on inverse classification can be looked at from three perspectives: the manner in which the algorithm operates, the type of data the algorithm operates on, and the framework that guides the process of obtaining recommendations. Algorithm operation, which represents the optimization method employed, can be broken down into two groups: @cite_11 @cite_23 @cite_5 @cite_2 and @cite_7 @cite_4 . Greedy methods tend to focus on extreme objectives, which may not be realistic in the real world, while nongreedy methods tend to focus on more moderate objectives. This work uses the latter.