Patent AU-2021102593-A4 (AU)
Title: A Method for Detection of a Disease
Inventor: SINGH PRABH DEEP
IPC: G06K9/62
Filed: 2021-05-15
Granted: 2021-07-01
Abstract: The present disclosure seeks to provide a method for detection of a disease.  
Developing particle swarm optimization assisted support vector machine based diagnostic 
system for lung cancer prediction at the early stage. In this disclosure, the integration of 
Particle swarm optimization (PSO) with a support vector machine (SVM) is proposed to 
detect lung cancer at a very early stage. The proposed technique is verified by using the 
various standard lung cancer classification data sets. The comparison is drawn among the 
proposed and the existing technique based upon the various standard quality metrics of the 
data mining. Experimental results indicate that the proposed algorithm is more efficient than 
existing techniques. In the future, the suggested method would help recommend diagnosing 
cancer in various human organs.  
15
100 
O btaining a plurality of information of the disease, wherein pre-processing the plurality of 
informationtoenhancethe quality of information usirg pre-processing module. 102 
Extracting a plurality of features from the processed information using a feature extraction 104 
module,whereinapluralityofattributes relatedtothediseaseisobtainedduringextraction.  
Creating a population of the extracted attributes acqui red fro the feature extraction module 
using a K-rneanrs technique.10 
Evaluatinga position of each of the extracted attribute inthe population usinganobjectye 108 
function, wherei narn inirn urnrootrneanrsqua re error is used as theobjective function.  
Determi ni rg a best pos ition of each of the exracted attri bute anrd pl atirig each of the extracted 
attributeateachoftheestposition,whereinabestattributeisselectedforea chbestpositionon n 110 
order to opfti mize the extracted attri butes for disease detectiort 14 
Figure 1 
Steps Desenpoon 
Algoihm Pouanonotagents 
parameters p Posionofagenta in thesoluiionspace 
f Objecne function 
1 Velocityofagentsa 
Pa o Neighborboodofagent a(fixed) 
The neighborhood concept i PSO is not the samine as the one used in another 
metaheunsncs sech sance nPSO cachparticle'sneighborhoodnever 
Chan ges (is fixed) 
Required Number ofparticles usually between 10 and 50 
attributes C, is the importance ofpesenal bes value 
with C: is the unportance ofsaghboboodbest value 
values Usually C. - C: 4 (empirically chosenvalue) 
If velocity is too low- algorithm itoo slow 
If velocity is too high -algorithm too unable 
Particle P = P - t' wvrh 
update v =-cl * rand •(pets- p) 
rule - c2 - rand • tgqest - p) 
whert parocle-s position, path direction c; the weight of local 
information. :the weight ofglobal information,pBesr the best position of 
the artcle.gBer the best position ofthe swuarm.and rand random variable 
Algofithms P = Parrclejntrah:artron(e 
For - I to msrt 
For each partre p ia P do 
Ip - 1(p) 
If fp is better than fpaest) 
pBest p; 
end 
endyffest - best p in P.  
For enA ;prtree p i n P do 
r- r• cl rand • p~est-p) 
- c2 • rand • (gest - p).  
p = p - r.%endend 
PSD based I Createa'popaoen'ofatunbutes(paetdes)fremdevelopedclustersof 
K-means data set X usingthe K-reansalgoithm 
algorithm 2 Evaluat each particle's position according to the obedse fiMctionse 
for minimum root mean squared errer (RMSE) 
training 3 If a particle's curri position is better than its previous bes position, 
update it 
4 Determine the best particle accedingg to the partide's preios bt 
positions) 
5 Rerurnopmizedtrained model 
Figure2