Research Paper : Prediction of Revisit of Repeated Attempted-Suicide Patients Prediction of Revisit of Repeated Attempted-Suicide Patients Vuttichai Vichianchai*, Sumonta Kasemvilas**, Sakarin Kaewhao***, and Orapin Youdkang*** Abstract deaths and suicides have increased every year [2]. In Thailand, This works proposes a technique to predict the revisit of 16 deaths have been committed in every 100,000 population repeated attempted-suicide patients. The technique applies [3]. This phenomenon is one of the major problems in Thailand the factors relating to suicide and attempted suicide, which that must be solved and prevented. In recent years, a number are collected from medical treatment information. The proposed of researches have been conducted to study and identify technique considers the probability distribution of attempted- suicidal factors [4], [5]; however, these factors cannot be used suicide dates of the patients in order to examine a pre-determined directly for preventing suicide among the surveillance. threshold for classifying the patients into three categories of In medical treatments, medical records of patients are important revisit duration, i.e. (i) low, (ii) medium, and (iii) high. In information to undergo treatments for mental illness patients. addition, this work proposes a feature filtering method that This information is not only used for the treatment purposes, can select a set of significance factors from the suicide and but also can be aided for preventing further possible harms self-harm surveillance report (RP. 506S) of Khon Kaen (such as hurting themselves or even suicide) by assessing and Rajanagarindra Psychiatric hospital to perform the classification. evaluating the behaviors of patients, including predicting the There are 10,112 patients who had been in the services more possibility of attempted or repeated suicides. However, with than once. The filtering is performed before the threshold is the large amount of non-qualitative data, human cannot determined using a Gaussian function. The experiment results perform an analysis efficiently, which is prone to inter and show that the proposed technique is superior to the baseline intra-reproducibility. For this reason, it is necessary to for every learning algorithms, i.e. (i) k-NN, (ii) SVM, develop an algorithm that assists the analysis by deploying (iii) random forest and (iv) neural networks. In addition, the the data and transform them into qualitative information. results obtained from random forest provide promising outcomes. Therefore, this paper is aimed at designing and developing The best performance (in terms of F-measure) is 91.10%, a technique that assess the possibility of the patients (who obtained from random forest. have repeated attempted-suicide) to revisit and receive treatment services. The revisit can be used to examine patient Keyword: Data Classification, Revisit Attempted-suicide, conditions and for treatment plans. Therefore, this work Feature Filtering; Support Vector Machine, k-Nearest Neighbors. proposes a technique that applies learning algorithms to classify patients into different groups of revisit duration i.e. 1. Introduction (i) low, (ii) medium, and (iii) high. The features rating to the Suicide is the act of intentionally causing its own death revisit of patients are examined. These features are filtered to [1]. The causes of suicide come from different factors and produce a final set of discriminative features before the conditions such as depression and stress etc. A number of classification is performed. * Mahasarakham University. ** Khon Kaen University. *** Khon Kaen Rajanagarindra Psychiatric Hospital. 22 วารสารเทคโนโลยีสารสนเทศ ปีที่ 14 ฉบับที่ 1 มกราคม - มิถุนายน 2561 ปีที่ 14 ฉบับที่ 1 มกราคม - มิถุนายน 2561 วารสารเทคโนโลยีสารสนเทศ 23 Information Technology Journal Vol. 14, No. 1, January - June 2018 Vol. 14, No. 1, January - June 2018 Information Technology Journal Research Paper : Prediction of Revisit of Repeated Attempted-Suicide Patients This paper is organized as follows: Section 2 provides the The main objective of the research is to understand the proposition of related. Section 3 explains the overall data used suicidal factors to prevent possible harms. The data collected in this study. In Section 4, the proposed technique is are the data of who attempt to suicides. There were two ways delineated before the experiments are demonstrated in of collecting data, i.e., 1) Data collection from Emergency Section 5. The conclusion of the work is given in Section 6. department between October 1, 2010 and September 30, 2012. There were 374 patients, including gender, age, occupation, 2. Related work marital status, attempted suicide, suicide, history of physical In general, the factors of suicide can be studied by observing and mental health problems and information on depression. hidden information from selected samples of the people who 2) In-depth interviews of suicide attempters at the medical have attempted suicide. A. Khamma proposed a technique ward between May 1 and June 30, 2013. that studied suicide related factors by selecting samples from This study investigated five patients with suicidal behaviors. the suicide population in 2006-2011 [4]. The data was The questionnaire consists of general information, suicide collected in Sukhothai, Muang District, Si Samrong District, attempt information, family relationship information, illness Si Satchanalai District and Sawankhalok District by interviewing or substance use, and media mimicking. The results of the the patients. There were 330 patients. The collected data was research found that 1) the factor of suicide, i.e., male, analyzed by using SPSS to analyze quantitative data and risk employed, in the middle age or working age, has a history of factors. The work divided the sample (patients) into two self-harm and lives in the area of drug problems, especially groups, i.e. suicide and suicide attempts. In this work, the cigarettes and alcohol. 2) The factor of suicide attempt, i.e., interview form was modified from the standard interview jobs or a workers, teenagers and early working age, living in form for searching death information by Khon Kaen rural areas, levels of depression and personal problems with Rajanagarindra Psychiatric hospital, i.e., 1) Suicidal groups friends. were collected by interviewing relatives or deceased close Risk of suicide classification using machine learning is relatives of those who died within 3 months, psychiatric one of the techniques that have been reported in literature [6]. nurses, District Health hospital and Public Health Officer. 2) There are two techniques, i.e., the k-Nearest Neighbors Suicidal groups were collected by individual interviews technique [7] and the Linear Classifiers of Gaussian [8]. during psychiatric treatment sessions with emphasis on ethical The conceptual model of suicide attempts [9]. However, it principles in human research. The factors and additional cannot be analyzed because the data used in the analysis is information were collected, which are collect age, gender, based on interview data and cannot be tested. The problem occupational status, medical history, both physical and was solved by using data sets from the Barwon hospital, psychiatric diseases, problem alcohol use events or event Australia. The emergency and inpatient departments trigger of suicide, including suicide attempts. The results of included 42,000 records and the psychiatric patients included the research illustrated that males between the ages 31 to 50 8,000 records from 2005 to 2012 [10]. This research divides years, hiring or labor or farming and quarrels presented a high the risk of suicide into three groups: low risk is thought to be degree of risk for suicide. suicide, moderate risk and high risk. The data set used for the To study the suicide rate and factors related to suicidal test included 17,781 records of 7,746 patients, including age, behaviors of the Chaophrayayommarat hospital in Suphanburi gender, language, religion, occupation, marital status Indigenous Province. A research was conducted by collecting the status and postal code of origin. The results of the information of the treatment records of the patients [5]. research showed that the proposed framework outperformed 24 วารสารเทคโนโลยีสารสนเทศ ปีที่ 14 ฉบับที่ 1 มกราคม - มิถุนายน 2561 ปีที่ 14 ฉบับที่ 1 มกราคม - มิถุนายน 2561 วารสารเทคโนโลยีสารสนเทศ 25 Information Technology Journal Vol. 14, No. 1, January - June 2018 Vol. 14, No. 1, January - June 2018 Information Technology Journal Research Paper : Prediction of Revisit of Repeated Attempted-Suicide Patients the risk assessment tool by medical professionals. 7) Getting help: counseling, guideline documents, etc. In addition, decision trees [11], regression log analysis 3.2 Errors and missing data [12], random forest techniques [13], gradient boosting This data set contains 164 attributes, and has a variety of machines [14], and deep neural networks [15] area among of data types, such as numeric data types, numeric data types the technique that are used to predict the risk of suicide in represent descriptive and textual data types. The RP. 506S different period i.e., 15, 30, 60, 180, and 360 days. These has been developed and updated several versions from 2006 techniques have used data from Barwon hospital in Australia to 2017, which alters and changes some of the recorded data [10]. There are 10,000 records for importing information. resulting inaccurate information (Such as null