Cancer diseases are considered one of the most critical problems facing the world's countries, especially the State of Iraq. Many local and international reports indicated that the weapons used in wars and the accompanying nuclear and chemical radiation are among the most prominent reasons for the spread of cancerous diseases in Iraq. This study found that Gender has the highest discriminating power, whereas the Grade variable has the least discriminatory power. Similarly, Behavior has the highest discriminatory power, whereas the Government has the least biased power. It became clear that the third group (those with breast cancer) had the highest probability of the correct classification. The probability of correct classification reached 92%, followed by the second group with brain cancer, where the probability of correct classification was 64%. Finally, the first group with bladder cancer had the lowest probability of correct classification. We conclude that increasing the sample size has a significant impact on the correct classification of observations. The effects of these weapons were tremendously harmful to public health and the environment. Its effect persisted after many years, so three groups of cancer patients (bladder, brain, and breast cancer) were analyzed from 2012 to 2017 using a statistical method to analyze multivariate data. The results showed gender and the nature of the tumor (Behavior) have the highest discriminating power. The results were entirely satisfactory, as the discriminatory predictive capacity obtained a level of success of 72.2%.

The bridge penalty is widely used as a penalty for selecting and shrinking predictors in regression models. Although its effectiveness is sensitive to the parameters you decide to use for shrinking and adjusting. The shrinkage and tuning parameters of the bridge penalty are chosen concurrently, and a continuous optimization process called particle swarm optimization is proposed as a means to do this. If implemented, the proposed method will greatly facilitate regression modeling with superior prediction performance. The results show that the proposed method is effective in comparison to other well-known methods, but this varies greatly depending on the simulation setup and the real data application.