Analysis and Prediction of Suicide Attempts

ABSTRACT:

Suicide is increasingly becoming a serious concern for the society. In fact, it is one of the largest cause of deaths in today’s world. Hence it is necessary to stop this menace by developing accurate prediction systems based on available data. The paper primarily analysis the suicide data, identify significant attributes contributing towards suicide attempt and predict future such attempts with significant precision. A comparison between 3 machine learning algorithms: – logistic regression, random forest, and Naïve-Bayes for suicide prediction has been made here. The scope of this research is to understand the effectiveness of these algorithms for preventing future suicides.

PROJECT OUTPUT VIDEO:

EXISTING SYSTEM:

• A study by Lakshmi Vijayakumar analyses the various articles on suicides that have been published in IJP (Indian Journal of Psychiatry). Study reveals that suicidal behaviors are much more prevalent in India than what is officially reported. Social, public and mental health response is crucial for prevention.

• Another research has taken data on annual suicide mortality in the United States as maintained by the National Vital Statistics System of the Centers for Disease Control and Prevention (CDC). Their findings discovered that in the United States, suicide occurs among 10.8 per 100,000 persons. Suicide is the 11th-leading cause of death, accounting for 1.4 percent of all deaths. A detailed examination of the data by sex, age, and race/ethnicity reveals significant socio demographic variation in the suicide rate.

DISADVANTAGES OF EXISTING SYSTEM:

• Traditional approaches to the prediction of suicide attempts have limited the accuracy and scale of risk detection for these dangerous behaviors.

• Accurate suicide attempt prediction may require complex combinations of hundreds of risk factors. Traditional statistical techniques are not ideal for such analyses.

PROPOSED SYSTEM:

The paper primarily analysis the suicide data, identify significant attributes contributing towards suicide attempt and predict future such attempts with significant precision. A comparison between 3 machine learning algorithms: – logistic regression, random forest, and Naïve- Bayes for suicide prediction has been made here. The scope of this research is to understand the effectiveness of these algorithms for preventing future suicides.

ADVANTAGES OF PROPOSED SYSTEM:

• Machine learning (ML) techniques are well suited for such problems. These techniques can test a wide range of complex associations among large numbers of potential factors to produce algorithms that optimize prediction.

• This method produced more accurate prediction of suicide attempts than traditional methods

• Model performance steadily improved as the suicide attempt become more imminent

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

• System            : Pentium Dual Core.

• Hard Disk       : 120 GB.

• Monitor           : 15’’ LED

• Input Devices : Keyboard, Mouse

• Ram                : 1 GB

SOFTWARE REQUIREMENTS:

• Operating system : Windows 7.

• Coding Language : Python

REFERENCE:

Tarun Agarwal et. al., “Analysis and Prediction of Suicide Attempts”, 2019 International Conference on Computing, Power and Communication Technologies (GUCON), IEEE 2019.