## 1 Introduction  
Chronic related disorders tend to create long term health issues in patients where the immune system and related body functionalities are weakened, and it usually ends in restricting their autonomy and freedom [1]. Normal lifestyle gets severely affected and the cost of healthcare steadily rises. Apart from this, it also affects the emotional stability of the patients. Provided a proper diagnosis, some chronic disorders are curable, but the treatment is usually very lengthy and painful [1]. Nevertheless, the majority of these diseases are not yet curable. The available drugs help palliate the symptoms. Consequently, the patient's lifespan can be increased [2]. Effective diagnosis and treatment is required for better public health. Among all the chronic diseases found in India, diabetes, breast cancer and heart disease are relatively common [3]. However, the treatment of these diseases is a challenging task due to the exponential rise in population. Moreover, the regular manual analysis of these diseases has led to inefficient medical decisions, given the large number of patients. Diagnosing a large number of patients with a potential chronic disease can be extremely hectic and time consuming. Further, the manual detection and diagnosis of symptoms may lead to errors. Consequently, machine learning technologies are critical to the successful mitigation of this problem [4]. The automatic classification of patients on the basis of symptoms is being gradually explored using machine learning [3;4]. Disease-related datasets can be analysed and the main symptoms that underlie a specific chronic disease can be uncovered through machine learning [4]. However, extracting the most common symptoms from raw unstructured data records is a significant challenge since this type of data reduces the efficiency of machine learning algorithms. Attribute selection is a reliable pre-processing technique where less relevant data in an unstructured raw dataset is eliminated [5]. It acts as an optimizing tool that reduces the data size by selecting the relevant attributes [6]. This improves classification performance, reducing latency and increasing accuracy [7]. The main reasons for applying attribute selection techniques are that they:  
* Enable faster execution of data mining algorithms,
* minimize the model complexity and enhance its interpretability,
* enhance the classification/prediction accuracy rate,
* reduce over-fitting,  
In general, an attribute selection process is divided into four phases which include the following [8]. * Generation of subsets,
* evaluation of subsets,
* termination condition,
* validation of results. In the first phase, a candidate attribute subset is generated using an effective search algorithm. In the next phase, every subset is evaluated on the basis of a predefined evaluation condition and verified with the previous optimal subset. It is replaced with the previous subset if it is found to be a better subset. This subset generation and evaluation procedure continues until a termination factor is reached. Finally, the selected attribute subset is verified and validated with predetermined testing data samples. The overall process is illustrated in Figure 1.  
After the generation of attribute subsets, the evaluation of these subsets is conducted on the basis of predefined criteria to compute their performance. Usually, the performance of an attribute set is determined by its ability to differentiate the distinct classes. In terms of dependency on learning classification algorithms, attribute selection is categorized into two distinct types which include wrappers and filters. The two categories of attribute selection are Wrappers and Filters based on dependency and interrelation with the inductive learning algorithms. On the one hand, Wrapper methods compute the attributes on the basis of the accuracy of the target classification algorithm [9]. On the other hand, Filter methods calculate attributes using statistical interrelationship between an attribute subset and the target class [10].