Background: Lung cancer is the leading cause of cancer death worldwide Therefore, identification of genetic as well as environmental factors is very important in developing novel methods of lung cancer prevention. However, this is a multi-layered problem. Therefore a lung cancer risk prediction system is here proposed which is easy, cost effective and time saving. Materials and Methods: Initially 400 cancer and non-cancer patients' data were collected from different diagnostic centres, pre-processed and clustered using a K-means clustering algorithm for identifying relevant and non-relevant data. Next significant frequent patterns are discovered using AprioriTid and a decision tree algorithm. Results: Finally using the significant pattern prediction tools for a lung cancer prediction system were developed. This lung cancer risk prediction system should prove helpful in detection of a person's predisposition for lung cancer. Conclusions: Most of people of Bangladesh do not even know they have lung cancer and the majority of cases are diagnosed at late stages when cure is impossible. Therefore early prediction of lung cancer should play a pivotal role in the diagnosis process and for an effective preventive strategy.
-The goal of this paper discusses about different types of data mining classification algorithms accuracies that are widely used to extract significant knowledge from huge amounts of data. Here illustrate 20 classifications of supervised data mining algorithms base on type-2 diabetes disease dataset perspective to Bangladeshi populations. In this paper we compare 20 classification algorithms by measuring accuracies, speed and robustness of those algorithms using WEKA toolkit version 3.6.5. Accuracies of classification algorithms are measured in 3 cases like Total Training data set, 10 fold Cross Validation and Percentage Split (66% taken). Speed (CPU Execution Time) and error rate also measured as like as accuracy. Firstly checked top perform algorithms that have best outcome for different cases and then ranked top outcomes algorithms. Finally ranked best 5 algorithms among 20 algorithms based on their accuracies.
Cancer Detection is still challenging for the upgraded and modern medical technology. Evan now the actual reason and total curing procedure of cancer is not invented .After researching a lot statistical analysis which is based on those people whose are affected in brain cancer some general Risk factors and Symptoms have been discovered. The development of technology in science day night tries to develop new methods of treatment. According to a developing country like Bangladesh it is very difficult to bear hug amount of cost for treatment of brain cancer. But it is very easy to protest brain cancer before affected and reduce treatment cost. But the number of brain cancer patients is increasing rapidly in Bangladesh lack of education, money and consciousness. Dreadful, costly and fatal brain cancer also depends on some factors that are known risk factors of brain cancer like other cancers. The detection of Skin Cancer from some important risk factors is a multi-layered problem. Initially according to those risk factors 150 people's data is obtained from different diagnostic centre which contains both cancer and non-cancer patients' information and collected data is pre-processed for duplicate and missing information. After pre-processing data is clustered using K-means clustering algorithm for separating relevant and non-relevant data to Brain Cancer. Next significant frequent patterns are discovered using Pattern Decomposition algorithm shown in Table 1. Finally implement a system using java to predict Brain Cancer risk level which is easier, cost reducible and time saveable.
The upgraded and modern medical technologies are the most challenging task to detect cancer and provide accurate treatment. In Bangladesh about two million women are affected by 2 nd most occurring deathful breast cancer due to them and their family member's unconsciousness and poverty. It requires about $400-500 for proper diagnosis and treatment. Most of the Bangladeshi women are uneducated and feel shy with society or husband to go doctor for checking breast cancer. So it also will be a good achievement of this work to find breast cancer with more efficiency. Breast cancer depends on some risk factors that may help to detect breast cancer using multi-layered approach. In this work, at first it is collected 100 peoples' information which consist of both cancer and non-cancer information having missing or duplicate information. So pre-processing and K-means clustering methods are performed to separate relevant and non-relevant data to Breast Cancer. Then risk factors are ranked using WEKA tools and are assigned a score according to rank. Finally, it is implemented an application software using Lotus Notes to predict Breast Cancer risk level which is easier, effective, efficient, secured, cheap and time saving with some suggestions. This technique will contribute equal opportunity to the underdeveloped and developing countries to detect, diagnosis, and treatment of breast cancer. General TermsComputer Science, Data Mining, Breast Cancer in Bangladesh KeywordsBreast cancer in Bangladesh, Public health, Data mining, Risk factors of breast cancer, WEKA toolkit, Woman Health Conditions
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