## 1 Introduction  
Health is crucial element in today's life. Researchers have devoted vast efforts in proposing new policies, algorithms, systems, and architectures for healthcare. According to the World Health Organization (WHO), in 2013, the global requirement and the actual number of health workforce were 60.4 million and 43 million, respectively [1]. These figures will be increased to 81.8 million and 67.3 million, respectively, by 2030. Hence, it is believed that the shortage of medical personnel is unsolved and remained serious in the coming decade. Automatic decision making via machine learning is believed to be the only way out to solve the shortage of medical personnel [2; 3]. Medical workers may argue that the automatic system has a conflict of interest with them; nevertheless, it is not the truth. First, the current workload of medical workers (ratio of workers to patients) is heavy and will become normal. Second, an automatic system focuses on routine works, so that medical workers can devote more time to professional consultation and surgery activities. Third, the increase in quality of medical services will lead to higher acceptance and satisfaction by the public. Thus, medical workers will earn a higher social status and better job satisfaction. Many diseases and abnormal human conditions can be examined by digital imaging diagnostic, like X-ray, Computed Tomography (CT) scan, Magnetic Resonance Imaging (MRI), Ultrasonography, Electrocardiogram, and Biopsy. In this paper, Wrist pulse signal (WPS) of human is considered which provides key information regarding health conditions. In the literature, WPS can be utilized for various applications, for instance, pre-meal and post-meal classification [4], physical exercise [5], diabetes classification [6], hypertension association [7; 8], lung cancer recognition [9], and inflammation classification [10; 11]. Various signal processing techniques on WPS can be found in [12; 13; 14], for instance, dynamic time warping, wavelet analysis, periodic decomposition, principal component analysis, and linear discriminant analysis. In this paper, four common types of organ inflammation are considered, namely, appendicitisitis, acute appendicitis, duodenitis, and pancreatitis. According to the WHO, the annual deaths hat are attributable to appendicitis, duodenitis and pancreatitis in 2000, 2005, 2010, and 2015 are shown in Table 1[15]. It is noted that acute appendicitis is embedded into Global Health Expand (GHE) code 1240. From Table 1, the number of deaths in each category is increasing by an increment of29%, 24%, and 60% for appendicitis, duodenitis, and pancreatitis, respectively, from 2000 to 2015. Among three types of organ inflammations, pancreatitis is the leading cause, which is followed by duodenitis and appendicitisis. To conclude, the issues of deaths in these organ inflammations remain unsolved. There have been more than million of sufferers and thus it is necessary to have a reliable and accurate method for the diagnosis of organ inflammations. Based on literature finding, there are a few publications working on binary classification of healthy, appendicitisis, acute appendicitisis, duodenitis, and pancreatitis sufferers [10; 11]. In [10], the features extraction process, an auto-regression (AR) based model was proposed. Two features, the standard deviation and mean of the prediction error from AR model, were chosen to represent the information of the WPS, and for further analysis. With regard to the classification, the support vector machine (SVM) with linear kernel was adopted for the binary classification, which yields an accuracy of 77.8-91.2%. For further improvement, a radial basis kernel (RBF) has been utilized to replace the linear kernel [11]. The idea is that most of the classification problems are not linearly separable. The enhanced method achieved an accuracy of 88.6-98.4%.