Analysis and Design of Detection for Liver Cancer using Particle Swarm Optimization and Decision Tree

Liver cancer is taken as a major cause of death all over the world. According to WHO (World Health Organization) every year 9.6 million peoples are died due to cancer worldwide. It is one of the eighth most leading causes of death in women and fifth in men as reported by the American Cancer Society. The number of death rate due to cancer is projected to increase by45 percent in between 2008 to 2030. The most common cancers are lung, breast, and liver, colorectal. Approximately 7, 82,000 peoples are died due to liver cancer each year. The most efficient way to decrease the death rate cause of liver cancer is to treat the diseases in the initial stage. Early treatment depends upon the early diagnosis, which depends on reliable diagnosis methods. CT imaging is one of the most common and important technique and it acts as an imaging tool for evaluating the patients with intuition of liver cancer. The diagnosis of liver cancer has historically been made manually by a skilled radiologist, who relied on their expertise and personal judgement to reach a conclusion. The main objective of this paper is to develop the automatic methods based on machine learning approach for accurate detection of liver cancer in order to help radiologists in the clinical practice. The paper primary contribution to the process of liver cancer lesion classification and automatic detection for clinical diagnosis. For the purpose of detecting liver cancer lesions, the best approaches based on PSO and DPSO have been given. With the help of the C4.5 decision tree classifier, wavelet-based statistical and morphological features were retrieved and categorised

Masters and Doctor of Philosophy admission prediction of Bangladeshi students into different classes of universities

Many Bangladeshi students intend to pursue higher studies abroad after completing their undergraduate degrees every year. Choosing a university for higher education is a challenging task for students. Especially, the students with average and lower academic credentials (undergraduate grades, English proficiency test scores, job, and research experiences) can hardly choose the universities that could match their profile. In this paper, we have analyzed some real unique data of Bangladeshi students who had been accepted admissions at different universities worldwide for higher studies. Finally, we have produced prediction models based on random forest (RF) and decision tree (DT) techniques, which can predict appropriate universities of specific classes for students according to their past academic performances. Two separate models have been studied in this paper, one for Masters (MS)students and another for Doctor of Philosophy (PhD)students. According to the Quacquarelli Symonds (QS) World University Rankings, the universities where the students got admitted have been divided into 9 classes for MS students and 8 classes for PhD students. Accuracy, precision, recall and F1-Score have been studied for the two machine learning algorithms. Numerical results show that both the algorithm DT and RF have the same accuracy of 89% for PhD student data and 86% for MS student data