Machine Learning in Python: Diabetes Prediction Using Machine Learning

Introduction

Diabetes is a disease which happens when the glucose level of the blood becomes high, which eventually leads to other health problems such as heart diseases, kidney disease etc. Several data mining projects have used algorithms to predict diabetes in a patient. Though, in most of these projects, nothing is mentioned about the dangers of diabetes in women post-pregnancies. While data mining has been successfully applied to various fields in human society, such as weather prognosis, market analysis, engineering diagnosis, and customer relationship management, the application in disease prediction and medical data analysis still has room for improvement in accuracy.

Machine learning relates closely to Artificial Intelligence (AI) and makes software applications predict outcomes through statistical analysis. The algorithms used allow for reaching an optimal accuracy rate in predicting the output from the input data. Machine learning follows similar processes used in data mining and predictive modeling. They recognize patterns through the data entered and then adjust the actions of the program accordingly.

Machine learning algorithms are categorized as supervised learning and unsupervised learning. Supervised learning requires input data and the desired output data to build a training model. The training model is built by a data analyst or a data scientist. A feedback is then furnished concerning the accuracy of the model and other performance metrics during algorithm training. Revising is done as needed. Once the training phase is completed, the model can predict outcomes for new data. Classification is one of the many data mining tasks. Classification comes under supervised learning which implies that the machine learns through examples in Classification. In classification, every instance from the dataset is classified into a target value. Classification can either be binary or multi-label. Sometimes, one particular instance can also have multiple classes known as multi-class classification. Classification algorithms are majorly used for prediction and come under the category of predictive learning.

Unsupervised learning is used to draw inferences from the input data which do not have any labeled responses. This data is not categorized, labeled or classified into classes. Clustering analysis, one of the most common unsupervised learning method, is used to find hidden patterns in data or to form groups based on the input data.

While machine learning models have been around for decades, they have gained a new momentum with the rise of AI. Deep learning models are now used in most of the advanced AI applications. If these models are implemented for medical uses, they could be revolutionary for the society. Diagnosis of diseases like diabetes would be easier than ever. Machine learning in medical diagnosis applications fall under three classes: Pathology, Oncology and Chatbots. Pathology deals with the diagnosis of diseases with the help of machine learning models created with the data of diagnostic measurements of the patients. Oncology uses deep learning models to determine cancerous tissues in patients. Chatbots designed using AI and machine learning techniques can identify patterns in the symptoms of the patients and suggest a potential diagnosis or it can recommend further courses of action. This chapter falls under the pathological uses of machine learning as the model created will give diagnosis of whether a patient is diabetic or not.