In the United States, stroke is the fifth prominent cause of fatality and it is a major reason of serious disability among the adult population [1]. Therefore, it is crucial that we can predict stroke accurately in order to be treated in early stages. Nowadays, use of Machine Learning (ML) algorithms have been in great demand to predict patient's condition in advance and inform the medical staff to avoid the risk of disease progression. Kaggle Healthcare dataset has been widely used by many researchers in this area for developing models for stroke prediction. The dataset has 43,400 instances and 10 features. This paper proposes a method for the analysis and prediction of stroke on the same dataset using Microsoft Azure Machine Learning (AzureML) which is a cloud-based platform. We have applied Chi-Squared test on the dataset for extracting the top features. The experiments are run on AzureML with the top 6 features as well as with all the features. In addition, we compare accuracy between the two models trained by the top 6 features and all the features. The performance of Two-class Decision Jungle with top 6 features has been set as the benchmark in our work. Two-Class Boosted Decision Tree, an ensemble learning method achieves 96.8% accuracy using the top 6 features. Our experimental results show that with the right features, we could improve the accuracy significantly for the stroke prediction, and it also takes less time to train the model.

Human activity recognition (HAR) is a classification task that involves predicting the movement of a person based on sensor data. As we can see, there has been a huge growth and development of smartphones over the last 10–15 years—they could be used as a medium of mobile sensing to recognize human activity. Nowadays, deep learning methods are in a great demand and we could use those methods to recognize human activity. A great way is to build a convolutional neural network (CNN). HAR using Smartphone dataset has been widely used by researchers to develop machine learning models to recognize human activity. The dataset has two parts: training and testing. In this paper, we propose a hybrid approach to analyze and recognize human activity on the same dataset using deep learning method on cloud-based platform. We have applied principal component analysis on the dataset to get the most important features. Next, we have executed the experiment for all the features as well as the top 48, 92, 138, and 164 features. We have run all the experiments on Google Colab. In the experiment, for the evaluation of our proposed methodology, datasets are split into two different ratios such as 70–10–20% and 80–10–10% for training, validation, and testing, respectively. We have set the performance of CNN (70% training–10% validation–20% testing) with 48 features as a benchmark for our work. In this work, we have achieved maximum accuracy of 98.70% with CNN. On the other hand, we have obtained 96.36% accuracy with the top 92 features of the dataset. We can see from the experimental results that if we could select the features properly then not only could the accuracy be improved but also the training and testing time of the model.

Breast Cancer (BC) is the second most common cancer in women after skin cancer and has become a major health issue. As a result, it is very important to diagnose BC correctly and categorizing the tumors into malignant or benign groups. We know that Machine Learning (ML) techniques have unique advantages and that is why they are widely used to analyze complex BC dataset and predict the disease. Wisconsin Diagnosis Breast Cancer (WDBC) dataset has been used to develop predictive models for BC by researchers in this field. The dataset has 573 instances and 32 features. In this paper, we have proposed a method for analyzing and predicting BC on the same dataset using Apache Spark. This big data framework is a very powerful tool for working on huge volume of data, such as healthcare data [4]. Principle Component Analysis (PCA) has been applied on the dataset for selecting the most important features. We have run experiments with top 6 and 10 features. The experiments are executed on Hadoop cluster, a cloud platform provided by the Electrical Engineering and Computer Science (EECS) department of University of Cincinnati. We have also made a comparison between the performance of different machine learning techniques: Decision Tree and Random Forest Classifier. We have set the performance of Decision Tree with top 10 features as a benchmark in our work. Random forest Classifier performs better than Decision Tree algorithm with top 6 as well as top 10 features. Random Forest achieves 97.52 % accuracy using top 10 features. Our results show that selecting the right features significantly improves accuracy in predicting BC.

Nowadays, healthcare sector starts relying on the datasets that are collected by clinics or some organizations to help doctors in predicting and analyzing the patient’s status in early stage. There are many dangerous diseases around the world that people suffer from them, but one of the most dangerous diseases is cancer. Recent research shows that about 12% US women over the course of their life, develop invasive breast cancer. Thus, in this case, the breast cancer (BC) is categorized as a dangerous type among all cancer types. This study focuses on BC by using a well-known dataset titled Breast Cancer Wisconsin (Diagnostic) Data Set. It has 32 attributes and 569 instances. Some of those attributes have missing values and others are not necessary for our work. So, we removed the ID column and any instance that has a missing value. Our aims in this research is analyzing BC dataset and understand its features. Then, we upload it to Microsoft Azure machine learning (AzureML) platform for building our model. We use two classes Decision Jungle and two Classes Decision machine learning algorithms to predicate whether the patient diagnose is Benign or Malignant. We assess the performance of each algorithms in terms of different measures like Accuracy, Precision, Recall, F1 and AUC. The results of our study in this paper show that the accuracy of Decision Jungle is approximately 97%. On the other hand, the accuracy of Decision tree is approximately 95%.

The advent of transformer technology and large language models (LLMs) has further broadened the already extensive application field of artificial intelligence (AI). A large portion of medical records is stored in text format, such as clinical trial texts. Part of these texts is information regarding eligibility criteria. We aimed to harness the immense capabilities of an LLM by fine-tuning an open-source LLM (Llama-2) to develop a classifier from the clinical trial data. We were interested in investigating whether a fine-tuned LLM could better decide the eligibility criteria from the clinical trial text and compare the results with a more traditional method. Such an investigation can help us determine the extent to which we can rely on text-based applications developed from large language models and possibly open new avenues of application in the medical domain. Our results are comparable to the best-performing methods for this task. Since we used state-of-the-art technology, this research has the potential to open new avenues in the field of LLM application in the healthcare sector.