Determinación de perfiles socioeconómicos y sanitarios de las personas atendidas en las campañas efectuadas como actividades curriculares de la carrera de Medicina de la UNCAus en su área de influencia

El proyecto PI N° 91, aprobado por Res. N° 332/18 CS, se desarrolla con el fin de obtener información relevante que detecte variables relacionadas con los frecuentes problemas de salud de la ciudad de Presidencia Roque Sáenz Peña (Chaco, Argentina) y su área de influencia, relacionando los pacientes con su hábitat, ecología y salud. Para ello se trabaja con la información proveniente de las actividades curriculares de vinculación comunitaria que la carrera de Medicina de la UNCAus (Universidad Nacional del Chaco Austral) realiza en los distintos barrios de la ciudad y zona de influencia; con dicha información se construye un almacén de datos (data warehouse) que es estudiado con técnicas de minería de datos, especialmente técnicas de agrupamiento (clusterización) y de árboles de decisión, a los efectos de conseguir los perfiles característicos relacionados con los distintos tipos de diagnósticos; se buscan modelos descriptivos y predictivos de minería de datos, lo cual permitiría disponer de conocimiento que permitiría mejorar la toma de decisiones en cuanto a campañas de salud hacia la población de las zonas.Red de Universidades con Carreras en Informátic

Predicting death and morbidity in perforated peptic ulcer

Peptic ulcers are defined as defects in the gastrointestinal mucosa that extend through the muscularis mucosae. Although not being the most common complication, perforations stand out as being the complication with the highest mortality rate. To predict the probability of mortality, several scoring systems based on clinical and biochemical parameters, such as the Boey and PULP scoring system have been developed. This article explores, using data mining in the medical data available, how the scoring systems perform when trying to predict mortality and patients’ state complication. We also try to conclude, from the two scoring systems presented, which predicts better the situations described. Regarding the results, we concluded that the PULP scoring allows a better mortality prediction achieving, in this case, above 90% accuracy, however, the results may be inconclusive due to the lack of patients who died in the dataset used. Regarding the complications, we concluded that, on the other hand, the Boey system achieves better results leading to a better prediction when it comes to predicting patients’ state complication.FCT - Fundação para a Ciência e a Tecnologia (UID/CEC/00319/2013

Predicting postoperative complications for gastric cancer patients using data mining

Gastric cancer refers to the development of malign cells that can grow in any part of the stomach. With the vast amount of data being collected daily in healthcare environments, it is possible to develop new algorithms which can support the decision-making processes in gastric cancer patients treatment. This paper aims to predict, using the CRISP-DM methodology, the outcome from the hospitalization of gastric cancer patients who have undergone surgery, as well as the occurrence of postoperative complications during surgery. The study showed that, on one hand, the RF and NB algorithms are the best in the detection of an outcome of hospitalization, taking into account patients’ clinical data. On the other hand, the algorithms J48, RF, and NB offer better results in predicting postoperative complications.FCT - Fundação para a Ciência e a Tecnologia (UID/CEC/00319/2013

Determining eligible villages for mobile services using k-NN algorithm

To maximize and get population document services closer to the community, the Disdukcapil district of Alor provides mobile services by visiting people in remote villages which difficult-to-reach service centres in the city. Due to a large number of villages and limited time and costs, not all villages can be served, so the kNN algorithm is needed to determine which villages are eligible to be served. The criteria used in this determination are village distance, difficulty level, and document ownership (Birth Certificate, KIA, family card, and KTPel). The classes that will be determined are "Very eligible", "Eligible", and "Not eligible". By applying Z-Score normalization with the value of K=5, the classification gets 94.12% accuracy, while non-normalized only gets 88.24% accuracy. Thus, applying normalization to training data can improve the kNN algorithm's accuracy in determining eligible villages for "ball pick-up" or mobile services

Perancangan Data Mining untuk Klasifikasi Prediksi Penyakit ISPA dengan Algoritma C4.5

Terdapat beberapa kasus kematian anak dibawah umur 1  tahun meninggal akibat terkena penyakit ISPA (Infeksi Saluran Pernafasan Akut) , pada tahun 2015 mencapai 579 kasus kematian anak disebabkan oleh ISPA. Saat ini teknologi dapat memberikan informasi yang cepat dan akurat khususnya di lingkungan kesehatan baik untuk tim kesehatan, dokter, perawat bahkan untuk pasien sendiri agar lebih mudah mengontrol kesehatan mereka. Data mining berhubungan dengan pencarian data untuk menemukan pola atau pengetahuan dari data keseluruhan. Ternyata kumpulan data yang besar dapat menghasilkan sebuah data yang hasilnya dapat memberikan informasi pengetahuan yang baru. Data mining adalah sebuah langkah penting dalam proses menemukan pengetahuan. Pada penelitian ini akan dibahas tentang perancangan data mining menggunakan algoritma C4.5 untuk memprediksi penyakit ISPA akut atau tidak akut pada anak dengan memilih kandidat kriteria yang digunakanpada penelitian ini sehingga dapat memberikan kontribusi kepada tim medis di lingkungan kesehatan untuk mengatahui dan menindak lanjut pasien yang terkena penyakit ISPA

Uso de Minería de Datos Para la Determinación de Perfiles Socioeconómicos y Sanitarios en la UNCAus

El proyecto PI N° 91, aprobado por Res. N° 332/18 CS, se desarrolla con el fin de obtener información relevante que detecte variables relacionadas con los frecuentes problemas de salud en la ciudad de Presidencia Roque Sáenz Peña (Chaco, Argentina) y su área de influencia, relacionando los pacientes con su hábitat, ecología y salud. Para ello se trabaja con la información proveniente de las actividades curriculares de vinculación comunitaria que la carrera de Medicina de la UNCAus (Universidad Nacional del Chaco Austral) realiza en los distintos barrios de dicha ciudad y su zona de influencia; con dicha información se construye un almacén de datos (data warehouse) que es estudiado con técnicas de minería de datos (data mining), especialmente técnicas de agrupamiento (clusterización) y de árboles de decisión, a los efectos de conseguir los perfiles característicos relacionados con los distintos tipos de diagnósticos; en un principio se buscan modelos descriptivos de minería de datos, para pasar en el futuro a modelos predictivos, lo cual permitiría disponer de conocimiento que permitiría mejorar la toma de decisiones en cuanto a campañas de salud hacia la población de los barrios de la ciudad de P. R. Sáenz Peña. Project PI N ° 91, approved by Res. N ° 332/18 CS, was developed in order to obtain relevant information that detects variables related to frequent health problems in the city of Presidencia Roque Sáenz Peña (Chaco, Argentina) and its area of influence, relating patients to their habitat, ecology, and health. For this, we work with the information coming from the curricular activities based on the relationship with the community that the Medicine undergraduate degree program of the UNCAus (National University of Chaco Austral) carries out in the different neighborhoods of the said city and its area of influence. With this information, a data warehouse was built and studied with data mining techniques, especially clustering techniques and decision trees, in order to achieve the characteristic profiles related to the different types of diagnosis. Initially, descriptive data mining models was sought and moved on to predictive models in the future. This would result to knowledge that would lead to better decision-making regarding health campaigns towards the population of the neighborhoods of the city of P.R. Sáenz Peña

Performance Comparison of Association Rule Algorithms with SPMF on Automotive Industry Data

By the recent developments about the information technologies, companies can store their data faster and easier with lower costs. All transactions (sales, current card, invoicing, etc.) performed in companies during the day combine at the end of the day to form big datasets. It is possible to extract valuable information through these datasets with data mining. And this has become more important for companies in terms of today's conditions where the competition in the market is high. In this study, a dataset of a company selling car maintenance and repair products in Turkey is used. Association Rules are applied on this dataset for determining the items which are bought together by the customers. These rules, which are calculated specifically for the company, can be used to redefine the sales and marketing strategies, to revise the storage areas efficiently, and to create sales campaigns suitable for the customers and regions. These algorithms are also called Frequent Itemset Mining Algorithms. The most recent 11 algorithms from these are applied to this dataset in order to compare the performances according to metrics like memory usage and execution times against varying support values and varying record numbers by using SPMF platform. Three different datasets are created by using the whole dataset like 6-months, 12-months and 22-months. According to the experiments, it can be said that executon times generally increases inversely with the support values as nearly all algorithms have higher execution time values for the lowest support value of 0.1. dEclat_bitset algorithm has the most efficient performance for 6-months and 12-months dataset. But Eclat algorithm can be said to be the most efficient algorithm for 0.7 and 0.3 support values; on the other hand dEclat_bitset is the most efficient algorithm for 0.3 and 0.1 support values on 22-months dataset

Mobile Solution for data mining and decision support: Weight monitoring and early prediction of cardiac arrest.

The daily accumulation of data through various means has led to the popularity of data mining in recent times. Through the use of the data mining techniques, data that are collected are used for problem-solving and other purposes. In data mining, patterns and trends of large datasets are studied using computer-based techniques. This thesis is using an Android mobile application as a data sampling tool for data mining purposes. Using this application, a predictive machine learning model, was developed to predict the probability of occurrence of cardiac of arrest in users of a mobile app over a ten- year span. The designed mobile application also functions as a support tool for weight management and fitness. The mobile application was connected to a real-time database and a machine learning tool using a Python program to perform prediction. The machine learning was based on Logistic Regression that is one of the predominant models used in the healthcare sector for predictions. The system used the user’s age, height, weight, activity level and diabetes status to predict the user’s chances of getting a Sudden Cardiac Arrest (SCA) over a ten-year period. A detailed account of the implementation processes and principles are discussed throughout this work.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Monitoring spread of epidemic diseases by using clinical data from multiple hospitals: a data warehouse approach

A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Master’s in Information and Communication Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyMany countries apply data science techniques to enhance their health sectors and the surveillance of diseases. The success of the innovations lies on the availability and quality of datasets to be analyzed. In Tanzania, while different Hospital Management Information Systems (HoMIS) like the Government of Tanzania Hospital Management Information System (GoTHoMIS) are installed in various hospitals, the data stored in the systems are not integrated. This causes unavailability of high quality, timely, anonymous, harmonized, and integrated datasets that can be shared and exhaustively analyzed for epidemic diseases surveillance. This study intended to develop a data warehouse to host patients’ demographic and clinical particulars essential for epidemic diseases surveillance from a multi-node GoT-HoMIS, and yield an integrated dataset that can be used for epidemic diseases surveillance. Interviews were conducted in three strategic health facilities and the Ministry responsible for Health in Tanzania. Documents were reviewed, and observation done on the patient’s registration process in the GoT-HoMIS. Thereafter, a data warehouse was developed to run under MariaDB database server, and using Hypertext Preprocessor an Extract, Transform, and Load (ETL) module was developed. The ETL module was deployed at six health facilities, and the resulting integrated dataset of 152 104 facts was visualized by using FusionCharts libraries. The study demonstrates a novel means to extract data straight from the GoT-HoMIS nodes, which has the potential to make available and provide timely data and integrated reports for decision-making on epidemics. By scaling the innovation to other health facilities, epidemics surveillance can be significantly enhanced

FootbAI: football powerd by artificial intelligence

A competição futebolística moderna tem as características de confronto feroz, longa duração, intensidade de jogo e grandes quantidades de exercício, com elevados requisitos técnicos e táticos. É, portanto, um desporto complexo, sendo uma das tarefas mais importantes a seleção dos jogadores mais adequados para os jogos, o que envolve muitos fatores, como, por exemplo, as variáveis fisiológicas dos jogadores. O futebol, bem como outras áreas desportivas e sociais, tem sofrido a influência de tecnologias e sistemas de informação que têm contribuído para a melhoria do desporto, com ênfase principal nos sistemas de informação para apoiar a monitorização e análise do desempenho dos jogadores, tais como, por exemplo, a utilização de sistemas de informação para registar as variáveis fisiológicas dos jogadores utilizando sistemas GPS, e a medição dos batimentos cardíacos, entre outros. Dada a oportunidade de analisar os dados de uma equipa de futebol da 2ª Divisão Regional da Associação de Futebol de Santarém, surgiu a ideia de tentar compreender quais são as variáveis fisiológicas mais importantes no desempenho dos jogadores e, consequentemente, melhorar o desempenho das equipas, através de uma maior perceção da interligação entre as variáveis fisiológicas e os resultados desportivos. Em termos de resultados, foi possível desenvolver um modelo que agrupou as variáveis fisiológicas que tiveram mais influência na vitória, conseguindo ter resultados de 79% de precisão na previsão da vitória com estas variáveis. Também foi possível realizar a mesma análise realizada sobre os dados dos jogos, também sobre os dados de treino, e os resultados apontaram para diferentes variáveis. Em termos da análise dos atletas por posição, apesar dos resultados serem curtos, a aplicação de um algoritmo de seleção foi capaz de classificar as variáveis fisiológicas por posição, que estão em linha com as variáveis defendidas pela comunidade científica em estudos sobre estes temas