Our focus in this research work is to present an efficient algorithm for apt prediction of cancer of the kidney in which medical practitioners and patients could gain valuable knowledge for early and proactive intervention strategies to save lives from this harmful disease. To achieve these objectives, dataset pertaining to patients of cancer of the kidney were acquired from selected private and public hospitals in south west Nigeria. A two-layered classifier system consisting of Rule Induction (RI) and Decision Tree (DT) classifiers was designed to build the model based on data analytic approach. The classifier system designed was tested successfully using case study data from fifty-two (52) selected Local Governments in South West Nigeria using purposive and selective sampling technique. Ten classification algorithms were used in the modeling. Waikato Environment for Knowledge Analysis was used for the experiment and each model was built in two different ways (10-fold cross validation and percentage split mode). Performance comparison of the various algorithms considered was carried out using standard metrics of accuracy for classification and speed of model building benchmarks. The experimental results show that the J48 decision tree algorithm outperform all other algorithms in all the layers with correctly classified instances of 74.7%, F-Measure of 0.614, TP rate of 0.747, FP rate of 0.135, precision and recall of 0.687 and 0.714 respectively. It took the best algorithm, 0.03 seconds to build the model. This proves that the algorithm is suitable for the research purpose. The results from the system framework when tested with test data shows that the identified attributes, algorithm and the system model performed well and can serve as valuable tool for early detection of the disease in patients.
Data analytic has proven to be a multi-dimensional discipline that
uses descriptive techniques and predictive models to gain valuable
knowledge from data warehouses for recommendations and
decision making. It is the discovery of patterns and
communication of meaningful insight in data [
Age Group 31-40 2.
Age Lifestyle
Male, Female
Dataset pertaining to this research work was collected from selected health centres and hospitals in the south western part of Nigeria using purposive and selective sampling techniques. The researcher collected a sample data totaling, 1,006 records from fifty-two selected health centres in six (6) different states. The data collected was cleaned, normalized and organized in a form suitable for data analytic process. Table 1 shows the data format for the research data collection while Figure 1 and Figure 2 show the visualized information about selected states and health centres respectively.
Statistically, out of the 1,006 patient’s data captured, 44.8% were male while the remaining 55.2% are female, (See Table 2). The analysis further revealed that 57.1% of the patients are exposed to chemical and industrial contents while 32.7% of the population as gender and hereditary disorder. The patient’s life style data collected also indicated that the people around this region are addicted to smoking and drinking of alcohol, regular use of nonsteroidal anti-inflamatory drug (NSAIDs) such as ibuprofen and naproxen, which can double the risk of the disease by 51%. Other factors include obesity; faulty genes; a family history of kidney cancer; having kidney disease that needs dialysis; being infected with hepatitis C; and previous treatment for testicular cancer or cervical cancer. There is an indication also, that High blood pressure is a possible risk factor though still under investigation.
Waikato Environment for Knowledge Analysis (WEKA) platform
was used for the data analytic experiment. It is a powerful data
mining tool that has a GUI Chooser from which any of the four
major WEKA application environments (Explorer, Experimenter,
KnowledgeFlow and Simple CLI) can be selected. The Explorer
Application is selected for this experiment because it has a
workbench that contains a collection of visualization tools, data
processing, attribute ranking and predictive modeling with
graphical user interface (GUI) for easy access to this
functionalities, which are very important to the research work.
WEKA is a collection of machine learning algorithms for data
mining tasks. Algorithms implemented in WEKA include:
Bayesian classifiers, Decision Trees, Rules, Artificial Neural
Network (Functions), Lazy classifiers and miscellaneous
classifiers. But for the purpose of this work Rule Induction and
Decision Tree classifiers was considered. These families of
classifiers have been selected because of their performances in
various domains. They have both been successfully applied to a
variety of real-world classification tasks in industry, business,
science and education with good performances [10]. The classifier
system designed for the data modeling as shown in Figure 3 is of
two layers: Layer 1 consists of JRiP, PART and Decision Table of
the family of Rules Induction and Layer 2 consists of J48, LAD
Tree, Decision Stump, Random Forest, Rep Tree, BF Tree, and
LMT from the family of Decision Tree. The Decision Tree also
known as “white box” classification model can provide
explanation for their models, and could be used directly for
decision making [5], while the Rule Induction is one of the
fundamental tools of data mining, in which formal rules are
extracted from a set of observations. The rules extracted represent
a full scientific model of the data [
Patient’s Databank
Classifier System
Layer 1 Rule Induction
Layer 2
Decision Tree
As shown in Figure 3, the patient’s databank component is responsible for the data collection, updating and storing patient’s data from different sources. The classifier system component is responsible for the data modeling based on the algorithms in the layers. The performance evaluation component is responsible for the evaluation of the performance of the algorithms considered in the layers using standard metric to produce the best (optimal) algorithm. The rule generated from this algorithm is to be incorporated into the prediction system. Since the objective of the research work is to present a suitable algorithm for the cancer of the kidney prediction system, which the work has achieved. Hence the prediction system processes is not discussed in the work, but will be discussed in the future work of this research. 3.2
Ten (10) classification algorithms from the family of classifiers implemented in this work were used to model the patient’s dataset. The datasets for the experiment was first divided into two, which includes the training and testing datasets. 66% of the datasets was devoted to training while the remaining 34% was used for testing of randomly selected data. JRip, PART and Decision Table in layer 1 of the classifier system were first used to model the patient’s data and later the Decision Tree classifiers. The 10-fold cross validation test and percentage split modes were also considered in the modeling. Since they are from different classifiers family, they yielded different models that classify differently on some inputs. The algorithms were tested on the datasets in order to determine that which best models the data with best predictive accuracy.
The comparison of the performance of the various algorithms in
layer 1 and layer 2 based on the output from the percentage split
(hold-out) and 10-fold cross validation modes was carried out.
The results of the models from the two modes and the
performance evaluations are presented in Table 3. The 10-fold
cross-validation test mode was considered good since it produced
the best model both in layer 1 and 2 of the classifier system.
Moreover, the 10-fold cross validation mode have been widely
used, and it is described a better option to determine the
performance of a classifier [
FMeasure
0.614
From the experimental results and analysis, it shows that the J48
decision tree and LMT rules outperform all other algorithms in
the layers. However, J48 decision tree was chosen as the best
algorithm in this work because it has the correctly classified
instances of 74.7%, ROC Area of 0.78 and recall of 0.714
respectively. It has a lower FP rate of 0.153, F-Measure of 0.614
and took lesser time of 0.03 seconds to build the model compared
to LMT and other classifiers as shown in Table 4. Additionally,
J48 decision tree algorithms generally have this ability that can
produce a simple tree structure with high accuracy in term of
classification rate, even with huge volume of data [
Rule 3: IF (G&H Disorder = NO) AND (C&I Exposure = Yes) AND (Lifestyle = Smoking) AND Complaints = tumor: PL = Three Rule 4: IF (G&H Disorder = NO) AND (C&I Exposure = Yes) AND (Lifestyle = Smoking) AND Complaints = Fibroids: PL = Three Rule 5: IF (G&H Disorder = NO) AND (C&I Exposure = Yes) AND (Lifestyle = Smoking) AND Complaints = Stomach ucher : PL = Two Rule 6: IF (G&H Disorder = NO) AND (C&I Exposure = Yes) AND (Lifestyle = Smoking) AND Complaints = Kidney pain: One Rule 7 IF (G&H Disorder = NO) AND (C&I Exposure = Yes) AND (Lifestyle = Smoking) AND Complaints = Abdominal pain: Two Rule 8 IF (G&H Disorder = YES) AND (C&I Exposure = Yes) AND (Lifestyle = Smoking) AND Complaints = blood in urine: PL = One Rule 9 IF (G&H Disorder = YES) AND (C&I Exposure = Yes) AND (Lifestyle = Obesity) AND Complaints = blood in urine: PL = Two Rule 10 IF (G&H Disorder = YES) AND (C&I Exposure = Yes) AND (Lifestyle = HB Pressure) AND Complaints = blood in urine: PL = Two Rule 11 IF (G&H Disorder = YES) AND (C&I Exposure = Yes) AND (Lifestyle = Smoking) AND Complaints = Drug Abuse OR Tumor OR Fibroids: PL = Two Rule 12 IF (G&H Disorder = YES) AND (C&I Exposure = Yes) AND (Lifestyle = Smoking) AND Complaints = Abdominal pain: PL = Two Rule 13 IF (G&H Disorder = YES) AND (C&I Exposure = Yes) AND (Lifestyle = Smoking) AND Complaints = Kidney pain: PL = One Rule 14 IF (G&H Disorder = YES) AND (C&I Exposure = Yes) AND (Lifestyle = Smoking) AND Complaints = stomach ucher: PL = One Rule 15 IF (G&H Disorder = YES) AND (C&I Exposure = Yes) AND (Lifestyle = Alcohol OR Dialysis) AND Complaints = stomach ucher: PL = Two Rule 16 IF (G&H Disorder = YES) AND (C&I Exposure = Yes) AND (Lifestyle = Radiation) AND Complaints = stomach ucher OR blood in urine: PL = One Rule 17 IF (G&H Disorder = YES) AND (C&I Exposure = Yes) AND (Lifestyle = Water pills) AND Complaints = stomach ucher: PL = Three Rule 18 IF (G&H Disorder = YES) AND (C&I Exposure = NO) AND (Lifestyle = Smoking) AND Complaints = stomach ucher OR kidney pain: PL = One Rule 19 IF (G&H Disorder = YES) AND (C&I Exposure = NO) AND (Lifestyle = Smoking) AND Complaints = stomach ucher: PL = Two Rule 20 IF (G&H Disorder = YES) AND (C&I Exposure = NO) AND (Lifestyle = Smoking OR Obesity OR Drug Abuse OR Radiation OR Water Pills OR Dialysis) AND Complaints = stomach ucher: PL = Three
The research work was focused at presenting an efficient algorithm suitable for predicting the status of kidney cancer in patients. To achieve the objectives of the research work: (i). Dataset pertaining to patient was acquired from fifty LGA (52) selected Health Centres in the south western region of Nigeria using purposive and selective sampling techniques. (ii) the researcher developed a two-layered classifier system consists of Rule Induction and Decision Trees implemented on Waikato Environment for Knowledge Analysis (WEKA) to build the data model using data analytic approach, and (iii) different machine learning algorithms were used in search for the algorithm that produced the best model with predictive accuracy. In the experiment, ten (10) classification model algorithms from different classifier family were implemented on the patients’dataset. Since they are from different classifiers family, they yielded different models that classify differently on some inputs. The comparison of the performance of the various algorithms in layer 1 and layer 2, and the standard metrics of accuracy, precision, recall and f-measure for the best classifier considered in this work was carried out as shown in Table 3 and Table 4 respectively. The results show that the J48 decision tree outperform all other algorithms in the layers with predictive accuracy of correctly classified instances of 74.7 % in 0.03 seconds, ROC Area of 0.78, FP rate of 0.153, TP rate of 0.714, precision and recall of 0.614. .