European Journal of Molecular & Clinical Medicine

The main reason of increasing mortality rate among women is the breast cancer. It makes several hours with the less availability of systems to identify the diagnosis of cancer manually. Hence there is a need to develop an automatic system for early detection of cancer. Several researchers have focused in order to improve performance and achieved to obtain satisfactory results. But unfortunately it will be very difficult to detect the cancer in beginning stages because the symptoms may be inappropriate.Therefore, there is a need to determine and acquire a new knowledge to prevent and minimizing the risk of getting effected with cancer. Machine learning (ML) is algorithms are widely used in detecting breast cancer patterns and predict the grading level. Machine learning techniques can be used to classify the stage of cancer, where machine can be trained from past data and build a model so that it can predict the category of new input.In this paper we used K-nearest neighbors (K-NN) and Support Vector Machine (SVM) on the dataset collected from UCI repository to detect breast cancerwith respect to the results of accuracy the efficiency of algorithm is also measured and compared.

Autism spectrum disorder (ASD) is a psychiatric disorder which leads to
neurological anddevelopmental growth of a person which starts in early age and gets
carried throughout their life.It is a condition associated with significant healthcare costs
and early diagnosis can reduce these.Unfortunately, waiting time is lengthy for an ASD
diagnosis and it is cost effective. Due to theincrease in economy for autism prediction and
the increase in the number of ASD cases across theworld is in need of easily implemented
and effective screening methods by GUI results. Toovercome the time complexity for
identifying the disorder advanced technologies can be used suchas machine learning
algorithms to improve precision, accuracy and quality of the diagnosisprocess. Machine
learning helps us by providing intelligent techniques to discover the affectedpatient, which
can be utilized in prediction and to improve decision making. And hence, wepropose the
data set features related to autism screening of adult and child to be used for
furtheranalysis and to improve the classification of ASD cases.

With the quick improvement of the Internet, enormous information has been applied in a lot of use.
Be that as it may, there are regularly excess or unessential highlights in high dimensional information, so
include determination is especially significant. By building subsets with new highlights and utilizing AI
calculations including Xgboost and so on. To acquire early notice data with high dependability and constant by
applying large information hypothesis, systems, models and techniques just as AI strategies are the unavoidable
patterns later on. this examination proposed the fast choice of highlights by utilizing XGboost model in dispersed
circumstances can improve the Model preparing proficiency under conveyed condition.
GBTs model dependent on the inclination streamlining choice tree was superior to the next two models as far as
precision and continuous execution, which meets the necessities under the large information foundation. It runs
on a solitary machine, just as the conveyed preparing structures Apache Hadoop, Apache Spark.
We can utilize inclination plummet for our slope boosting model. On account of a relapse tree, leaf hubs produce
a normal inclination among tests with comparative highlights. Highlight determination is a basic advance in
information preprocessing and significant research content in information mining and AI assignments, for
example, order.