They are numbers of studies on the adolescents as the sample space to develop machine learning models to predict the suicide attempts. The methods used for that [Prediction by data mining, of suicide attempts in Korean adolescents: a national study]1, is Decision tree from 2754 from students of middle and high school from Korea. The results are identified suicide attempt as a dependent variable and eleven social variables andfound the suicide rates is 9.5% of the suicide attemptsand proves that suicide attempts have strong correlation with depression as stronger significant factor 5.4: 2.8 from non-depression group.Interestingly, in the potential depression group, the most influential predictor of suicidal involvement was a relationship with the family.and the study explainedamong the potential depression group, middle school students with lower intimacy with family had a 2.5-times higher rate of suicide attempts than high school students with lower intimacy with family. In other hand,thelevel of stress has a strong predictor for suicide attempts by average 8.3 times from the students have medium level of stress. In general, the most significant factors to suicide in the from the study above is the depression and weakness of family and the stress in the adolescents.More ever, use a machine learning approach on longitudinal clinical data of adolescents. Data were collected from the Vanderbilt Synthetic Derivative from January 1998 to December 2015, included 974 adolescents with nonfatal suicide attempts and multiple control comparisons: 496 adolescents with other self‐injury (OSI), 7,059 adolescents with depressive symptoms, and 25,081 adolescent general hospital controls. Candidate predictors included diagnostic, demographic, medication, and socioeconomic factors. Outcome was determined by multiyear review of electronic health records. Random forests were optimism adjustment at multiple time points (from 1 week to 2 years. the Random forests significantly outperformed logistic regression in every comparison. whereas the AUC is 95% at 7 days.[Predicting suicide attempts in adolescents with longitudinal clinical data and machine learning]2.

Another studying  on the elderly  when [Does disability predict attempted suicide in the elderly? A community-based study of elderly residents in Shanghai, China.]3 Define which the most significant factors effect in the suicide rates of elderly.The aim of [2] study explore the influence of disability on attempted suicide within this demographic on the elderly people of china. The sample space is 8399 residents aged 60 or more was investigated from 15 communities in Shanghai, China. The study using multivariable logistic regression model which found the preparing meals or dealing with medical care the most significant risk factors for attempted suicide among the elder. Another studying of suicide focus on specific region or country as the [] found, using regression, that overall178,323 suicides (50,265 femalesand 128,058 males) were committed in Hungary during the investigated period. The risk of suicide was higher among males than females overall in all age groups and for most suicidemethods. The annual suicide rate exhibited a significant peak in 1982 and remained quiteconstant after 2006. Different segmented patterns wereobserved for the suicide rates in thedifferent age groups.

[The use of regression methods for the investigation of trends in suicide rates in Hungary between 1963 and 2011]4.

Another studying dependent on monitoring specific indicators and predict the percentages of the suicides like the effect of weather or owing gun or working in specific occupation. The Analyses were based on three groupings of age-adjusted completed suicide rates (all suicide, firearm-related suicide, non-firearm-related suicide) from 2286 counties in the United States and applying the multiple regression to identify the overall relationship between atmospheric pressure and completed suicide rates. Geographically weighted regression (GWR) models were used to obtain local coefficient estimates. A negative correlation between atmospheric pressure and completed suicide rates was observed for all three suicide groupings (p-value <0.0001).

[Modeling the effects of atmospheric pressure on suicide rates in the USA using geographically weighted regression]5.

Also, there is relation between gun owners and suicide attempts at the people who have a Psychological disorderas the stress and depression. In other perspective, Lastly, our test of dependent correlationsindicated that the association betweenthe firearm suicide rate and the overall suiciderate (r = .92) was significantly higherthan the correlation between the non-firearmsuicide rate and the overall suicide rate(r = .46; t = 5.38; p < .00001).

[The Association Between Gun Ownership and Statewide Overall Suicide Rates]6.

In other perspective, there is studying on the association gun ownership of men and women and suicide rates in the USA by used the linear regression and generalized estimating equations, then found there is a relation between the gun ownership and the men ownership, not women.

[Firearm Ownership and Suicide Rates Among US Men and Women, 1981-2013]7.

Another factors effect in the suicide rates is the occupation situations for instance: the soldiers working in army especially after the wars, where is the studying on the 975 057 Regular US Army soldiers serving at any time during that time period from 2004–2009(32 million person-months), 569 of whom died by suicide [].This [] study includes comprehensive suicide risk assessments for ministry of defensepatients with mental disorders and provide minimal guidance on how to carry out these assessments. The clinician-based assessments are not strong predictors of suicide,the investigated whether a precision medicine model using administrative data after outpatient psychology health. Visits could be developed to predict suicides among outpatients. The study focused on male nondeployed Regular US Army soldiers because they account for most of such suicides. Machine learning methods were used to generate stable estimatesand comparing four different classifiers: (naive Bayes, random forests, support vector regression and elastic net penalized regression) were explored. The results mentioned that 173 suicides;( 65.6/100 000) person-years) and the suicide rate after these visits was substantially higher among men than women (75.3/ versus 19.6/100 000 person-years), with 94.8% (164 of 173) of suicide deaths after these visits occurring among men[Predicting suicides after outpatient mental health visits in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS).]8.

[Does Unstable Employment Have an Association with SuicideRates among the Young?]9

Another study on the unstable employment as There is a lot of literature has indicated that unemployment has a positive relation with suicide, The aspects of unstable employment have not yet been considered in suicidology. This study explored the strong correlation between employment stability and completed suicide among people aged 25–34 years in 20 OECD (Organization for Economic Cooperation and Development) countries with time-series data (1994–2010). In order to consider the different aspects of unstable employment, we tested the impacts of employment protection legislation indicators as another proxy of job insecurity (employed, but unstable) apart from unemployment rates. Covariates, including economic growth rates, GDP per capita, fertility rates, and divorce rate, were controlled for. The analysis was designed to be gender- and age-specific, where observations with ages of 25–29 were separated from those with ages of 30–34. Random effect models were applied to examine changes over time in suicide rates, and other models were presented to check robustness. The results showed that it is a low level of employment protection, rather than unemployment itself, that was associated with increased suicide rates among all the studied populations.

Another Attemptsto build models and predict the suicide rates by using the social media data on Suicide is not only an individual phenomenon, also influenced by social and environmental factors. With the high suicide rate and the abundance of social media data in South Korea, we have studied the potential of this new medium for predicting completed suicide at the population level. We tested two social media variables along with classical social, economic and meteorological variables as predictors of suicide over 3 years (2008 through 2010). Both social media variables were powerfully associated with suicide frequency. The suicide variable displayed high variability and was reactive to celebrity suicide events, while the dysphoria variable showed longer secular trends, with lower variability. We interpret these as reflections of social affect and social mood, respectively. In the final multivariate model, the two social media variables, especially the dysphoria variable, displaced two classical economic predictors – consumer price index and unemployment rate. The prediction model developed with the 2-year training data set (2008 through 2009) was validated in the data for 2010 and was robust in a sensitivity analysis controlling for celebrity suicide effects. These results indicate that social media data may be of value in national suicide forecasting and prevention.

[Predicting national suicide numbers with social media data]10

Another studying on the link from grade pressure to suicide risk showed the largest standardized path coefficient (beta = .357, p < .001) in structural models and a significant random effect (p < .01) in multilevel models. Depression was a partial mediatorbetween suicide risk and grade pressure, low body image, victims of bullying, and concerns about disease. The largest total effect was observed in the grade pressure to depression to suicide risk. The multilevel models indicate about 27% of the variance in the daily suicide-related word search activity is explained by month-to-month variations. A lower employment rate, a higher rental prices index, and more bullying were associated with an increased suicide-related word search activity.

[Data Mining of Web-Based Documents on Social Networking Sites That Included Suicide-Related Words Among Korean Adolescents]11.

The Time series 2.

Another working focus on the historical data of country and compare the trend of the suicide rates in time series, the studying on the

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