Abstract: Cancer formation is different for each type ofcancer, it has been determined by studies and research that stress alsotriggers cancer types. Early precaution is important for people who have notsuffering yet and who are in beginning stage with disease like cancer which hashigh chances of death. With this study Author expound that possibility ofdeveloping such disease may be decreased and people could take measures to it.For some of the cancer types fuzzy logic model is introduced, risk rate ofcausing these cancer types and preliminary diagnosis for the person who haschance of causing cancer will remove these risk. Using fuzzy analysis riskoutcomes are calculated, effect of stress on cancer is discussed and determinedusing soft computing techniques. A fuzzy logic approach that can be adopted toother industries as well used in health care and in effective software forapplication development.

Fuzzy rules can be extracted from the trained networksand which gives information about the classification process in easilyaccessible form while fuzzy logic rules point to genes that are associated withspecific type of cancer and also used for the developing new tests and for newtreatment methods.                                1.     IntroductionBioinformaticsis an interdisciplinary field of science, it combines Computer Science, Biology,Mathematics, and Engineering to analyse and interpret biological data.

Itdevelops methods and software tools to understand the biological data and dealswith soft computing, databases, algorithms, software engineering and imageprocessing. The algorithms in turn depend on theoretical foundations suchas discrete mathematics, control theory, system theory, information theory, and statistics. The identificationof molecular markers and profiles is being used in cancer classification and diagnosis as well as in clinicaloutcomes. The aim is to highlight some of the main molecular or bioinformatics methodologies in cancer research and theirapplication.

In systems Biology gene regulatory networks have animportant role in advance prediction of cancer. By modelling understanding andanalysis of these gene regulatory networks dynamics. Prediction of behaviour ofthe gene regulatory network will speed up in developing medicines.Bioinformatics use the soft computing techniques for better prediction andunderstanding in cancer research which is the field of bio-informatics.Usage of numerical systems in medical field mayreduce the loss of patients. Mathematical models may be used almost everywherethat a decision-making problem exists. Fuzzy logic plays an important role inthe field of medicine and some of the application of fuzzy logic techniques inmedicine are diagnosis of breast cancer, lung cancer or prostate cancer.

If wecan use soft computing technique such as fuzzy logic, artificial intelligence,many diseases like cancer may prevented by early diagnosis. Thus, expensivetreatments or surgeries may not even be required. In most of the cases peopleconcert hospital in the advanced stages of the disease so they are diagnosedlate.

As a result, treatments are useless most of the time and patient dies inshort time. Future-oriented diagnosis of cancer in healthy people is the mostimportant issue that should be focused.                                     2.     Relatedwork Many application are available in the field ofbio-informatics which deals with classification of cancer types, prediction anddiagnosis. Many analysis and methodologies have come up which analyse the geneexpression data using data mining techniques for feature selection,classification, clustering etc. also Soft computing methods are used for moreaccuracy.Awork titled “Evolving connectionist systems for knowledge discovery from gene expressiondata of cancer tissue” explains about Microarray technique in which it ispossible to observe the expression of thousands of genes simultaneously. Authorused knowledge-based neuro computing (KBN), in particular fuzzy neural networksare applied to classify cancer tissue, which is illustrated on the case studiesof leukaemia and colon cancer.

Fuzzy logic rules are used determine type ofcancer 2. Another proposes methods to determine the risk of developing typesof cancers in the future for healthy people and preliminary diagnosis based onthe studies conducted by using artificial intelligence and fuzzy logictechnique in medicine, determining the cancer type is studied and factorseffected on types of cancer will be investigated. Here breast cancer, lungcancer, and colon cancer are selected, reason for selecting these types is thefrequency of patient numbers and appropriateness of this study for indicatedcancer types 3. Data mining and clustering techniques are used for predictingcancer by comparing gene expression samples that are taken from patients withdocumental data.

For the collected samples gene expression patterns aredesigned and compared with the sample pattern to find out the affected genepatterns. Clustering technique is used to form clusters of related genepatterns, after analysing final prediction of cancer is done. Algorithm used inthe proposed system are Supervised Multi attribute Clustering Algorithm and AntColony Optimization Algorithm 4. Neuro-fuzzy logic used develop a model todetermine the risk factors for lung cancer by analysing details of  smoke, genetic status, age, living environmentand some other factors. Asa result of this, pre-diagnosis or opportunities to remove cancer risk will beprovided to a patient by examining risk analysis for lung cancer 5.  An agent based system for risk analysis ofbreast cancer has designed based on fuzzy multi agent which includes benefits formfuzzy set theory and its linguistic information.

MAS(multi agent system) decidestheCancerrisk rate whether it is high or low. Here actual data are gathered from various  hospitals and analysed by comparing thetest reports 6. Adaptive fuzzy Petri net(AFPN) reasoning algorithm used todevelop a system which predict outcomes of esophageal cancer, this system performsfuzzy reasoning to evaluate risk degree value for cancer 7. 3.      Current practice using Fuzzy ValuesFuzzy logictechnique is used in most of the medical concepts and it is convenient for manymedical application due to the relationship between the concepts and uncertainnature of medical concepts. Using fuzzy logic can find solution for evenuncertain problems.

Since the traditional analysis approaches are notappropriate and complexity of concepts in medicine is comparatively more.    a.      Fuzzy Interface System”Fuzzy Interface Systemis a process that uses fuzzy logic to map inputs and outputs”.

Some of theimportant concepts of fuzzy logic includes fuzzification of variables, usage offuzzy operators such as intersection (AND), Union (OR), additive complement.Fuzzification converts the inputs and outputs to linguistic variables or naturallanguage.                                Fig.1 Input and output map for theindoor health risk level in fuzzy inference system.      Risk level ofspecified matters classified according to the Table (1), which include thecriterion levels as very low, low, moderate, high, very high.  Criterion levels Fraction of threshold limit DA (very low) X<0.1 DB (low) 0.

12          C(health risk levels) = (CPM2.5 × WPM2.5) + (CPM10 × WPM10) + (CTSP× WTSP) Determining criteriaof the risk level of specified matter (PM25, PM10 and TSP) are classified according tothe effects on health and each of the factors determine based on risk levelaccording to equation.    3.2.

Fuzzy rules for Lung Cancer RiskAnalysis                Sample Values: fi = ai * age + bi* cigarette + ci * genetic + di*environment + ei*skin color + hi   Age = young & Cigarette = low & Genetic = low & Environment = well & Skin color = white skinned -> f1 = a1*age + b1*Cigarette + c1*Genetic + d1*Environment + e1*Skin Color + h1   f1 = a1*age + b1*Cigarette + c1*Genetic + d1*Environment + e1*Skin Color + h1 Age = young & Cigarette = low & Genetic = low & Environment = worse & Skin Color = black skinned->  Basedon details filled in the form Risk rate will be calculated, entries in the formdetails such as age, if cancer caught before, intestine status genetic status,Consumption of alcohol and smoking etc. This calculation of degrees enables therules. Output of active rule become output calculation of the whole system.

             Conclusion Aim of thisstudy is determining the risk rate and factors influencing on it for the occurrenceof the cancer. Usage of fuzzy logic in medicine to find the solutions for manyuncertain problems, Fuzzy rules play an important role while decision makingand analysing the factors effecting on the risk level of cancer. There are manymethods proposed for predicting the rate of causing cancer and pre-diagnosisusing soft computing approaches.

Reason for selecting fuzzy technique is itgives effective results by analysis the verbal inputs so the chances of mistakeshapping by the medical equipment is comparatively low. Probabilistic resultsobtained from the fuzzy logic are compared with various test results henceauthor’s proposed method can be used for private health system also as publichealth system for expertise and for researchers. Many researches are going onin this area and system can also be improved by including preventivesuggestions regarding heath habits and preventive methods.



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